Date: (Sun) May 29, 2016

Introduction:

Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv
Time period:

Synopsis:

Based on analysis utilizing <> techniques, :

Summary of key steps & error improvement stats:

Prediction Accuracy Enhancement Options:

  • transform.data chunk:
    • derive features from multiple features
  • manage.missing.data chunk:
    • Not fill missing vars
    • Fill missing numerics with a different algorithm
    • Fill missing chars with data based on clusters

[](.png)

Potential next steps include:

  • Organization:
    • Categorize by chunk
    • Priority criteria:
      1. Ease of change
      2. Impacts report
      3. Cleans innards
      4. Bug report
  • all chunks:
    • at chunk-end rm(!glb_)
  • manage.missing.data chunk:
    • cleaner way to manage re-splitting of training vs. new entity
  • extract.features chunk:
    • Add n-grams for glbFeatsText
      • “RTextTools”, “tau”, “RWeka”, and “textcat” packages
  • fit.models chunk:
    • Classification: Plot AUC Curves for all models & highlight glbMdlSel
    • Prediction accuracy scatter graph:
    • Add tiles (raw vs. PCA)
    • Use shiny for drop-down of “important” features
    • Use plot.ly for interactive plots ?

    • Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
    • create a custom model for rpart that has minbucket as a tuning parameter
    • varImp for randomForest crashes in caret version:6.0.41 -> submit bug report

  • Probability handling for multinomials vs. desired binomial outcome
  • ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
  • extensions toward multiclass classification are scheduled for the next release

  • fit.all.training chunk:
    • myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
  • Compare glb_sel_mdl vs. glb_fin_mdl:
    • varImp
    • Prediction differences (shd be minimal ?)
  • Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
  • Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
  • Parameterize glb_analytics_pn
  • Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
  • Do non-glm methods handle interaction terms ?
  • f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
  • Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
  • Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
  • Incorporate code chunks in print_sessionInfo
  • Test against
    • projects in github.com/bdanalytics
    • lectures in jhu-datascience track

Analysis:

rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")
## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores) 

suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
## 
##     arrange, count, desc, failwith, id, mutate, rename, summarise,
##     summarize
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")

# Analysis control global variables
# Inputs
#   url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>"; 
#               or named collection of <PathPointer>s
#   sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
    # or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
    #, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
    #                       select from c("copy", NULL ???, "condition", "sample", )
    #                      ,nRatio = 0.3 # > 0 && < 1 if method == "sample" 
    #                      ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample" 
    #                      ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'    
    #                      )
    )                   
 
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv") 

glbObsDropCondition <- NULL # : default
#   enclose in single-quotes b/c condition might include double qoutes
#       use | & ; NOT || &&    
#   '<condition>' 
    # 'grepl("^First Draft Video:", glbObsAll$Headline)'
    # 'is.na(glbObsAll[, glb_rsp_var_raw])'
    # '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
    # 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
    
glb_obs_repartition_train_condition <- NULL # : default
#    "<condition>" 

glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
                         
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression; 
    glb_is_binomial <- TRUE # or TRUE or FALSE

glb_rsp_var_raw <- "Party"

# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"

# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"), 
#   or contains spaces (e.g. "Not in Labor Force")
#   caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL 
function(raw) {
#     return(raw ^ 0.5)
#     return(log(raw))
#     return(log(1 + raw))
#     return(log10(raw)) 
#     return(exp(-raw / 2))
    ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "R"))
#     as.factor(paste0("B", raw))
#     as.factor(gsub(" ", "\\.", raw))
    }

#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw])))) 

#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))

glb_map_rsp_var_to_raw <- #NULL 
function(var) {
#     return(var ^ 2.0)
#     return(exp(var))
#     return(10 ^ var) 
#     return(-log(var) * 2)
#     as.numeric(var)
#     levels(var)[as.numeric(var)]
    sapply(levels(var)[as.numeric(var)], function(elm) 
        if (is.na(elm)) return(elm) else
        if (elm == 'R') return("Republican") else
        if (elm == 'D') return("Democrat") else
        stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
        )  
#     gsub("\\.", " ", levels(var)[as.numeric(var)])
#     c("<=50K", " >50K")[as.numeric(var)]
#     c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))

if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
    stop("glb_map_rsp_raw_to_var function expected")

# List info gathered for various columns
# <col_name>:   <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.

# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>") 
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"

# User-specified exclusions
glbFeatsExclude <- c(NULL
#   Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
#   Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
#   Feats that are linear combinations (alias in glm)
#   Feature-engineering phase -> start by excluding all features except id & category & 
#       work each one in
    , "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel" 
    ,"Q124742","Q124122" 
    ,"Q123621","Q123464"
    ,"Q122771","Q122770","Q122769","Q122120"
    ,"Q121700","Q121699","Q121011"
    ,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012" 
    ,"Q119851","Q119650","Q119334"
    ,"Q118892","Q118237","Q118233","Q118232","Q118117"
    ,"Q117193","Q117186"
    ,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
    ,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
    ,"Q114961","Q114748","Q114517","Q114386","Q114152"
    ,"Q113992","Q113583","Q113584","Q113181"
    ,"Q112478","Q112512","Q112270"
    ,"Q111848","Q111580","Q111220"
    ,"Q110740"
    ,"Q109367","Q109244"
    ,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
    ,"Q107869","Q107491"
    ,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
    ,"Q105840","Q105655"
    ,"Q104996"
    ,"Q103293"
    ,"Q102906","Q102674","Q102687","Q102289","Q102089"
    ,"Q101162","Q101163","Q101596"
    ,"Q100689","Q100680","Q100562","Q100010"
    ,"Q99982"
    ,"Q99716"
    ,"Q99581"
    ,"Q99480"
    ,"Q98869"
    ,"Q98578"
    ,"Q98197"
    ,"Q98059","Q98078"
    ,"Q96024" # Done
    ,".pos") 
if (glb_rsp_var_raw != glb_rsp_var)
    glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)                    

glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"

glbFeatsDrop <- c(NULL
                # , "<feat1>", "<feat2>"
                )

glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"

# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();

# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
#     mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) } 
#   , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]

    # character
#     mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) } 
#     mapfn = function(Week) { return(substr(Week, 1, 10)) }
#     mapfn = function(Name) { return(sapply(Name, function(thsName) 
#                                             str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) } 

#     mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
#         "ABANDONED BUILDING"  = "OTHER",
#         "**"                  = "**"
#                                           ))) }

#     mapfn = function(description) { mod_raw <- description;
    # This is here because it does not work if it's in txt_map_filename
#         mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
    # Don't parse for "." because of ".com"; use customized gsub for that text
#         mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
    # Some state acrnoyms need context for separation e.g. 
    #   LA/L.A. could either be "Louisiana" or "LosAngeles"
        # modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
    #   OK/O.K. could either be "Oklahoma" or "Okay"
#         modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw); 
#         modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);        
    #   PR/P.R. could either be "PuertoRico" or "Public Relations"        
        # modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);        
    #   VA/V.A. could either be "Virginia" or "VeteransAdministration"        
        # modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
    #   
    # Custom mods

#         return(mod_raw) }

    # numeric
# Create feature based on record position/id in data   
glbFeatsDerive[[".pos"]] <- list(
    mapfn = function(raw1) { return(1:length(raw1)) }
    , args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
#     mapfn = function(raw1) { return(1:length(raw1)) }       
#     , args = c(".rnorm"))    

# Add logs of numerics that are not distributed normally
#   Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
#   Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
#     mapfn = function(WordCount) { return(log1p(WordCount)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
#     mapfn = function(WordCount) { return(WordCount ^ (1/2)) } 
#   , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
#     mapfn = function(WordCount) { return(exp(-WordCount)) } 
#   , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
    
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
#     mapfn = function(District) {
#         raw <- District;
#         ret_vals <- rep_len("NA", length(raw)); 
#         ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm) 
#                                         ifelse(elm < 10, "1-9", 
#                                         ifelse(elm < 20, "10-19", "20+")));
#         return(relevel(as.factor(ret_vals), ref = "NA"))
#     }       
#     , args = c("District"))    

# YOB options:
# 1. Missing data:
# 1.1   0 -> Does not improve baseline
# 1.2   Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- 2016 - raw1 
        # raw[!is.na(raw) & raw >= 2010] <- NA
        raw[!is.na(raw) & (raw <= 15)] <- NA
        raw[!is.na(raw) & (raw >= 90)] <- NA        
        retVal <- rep_len("NA", length(raw))
        # breaks = c(1879, seq(1949, 1989, 10), 2049)
        # cutVal <- cut(raw[!is.na(raw)], breaks = breaks, 
        #               labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
        cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
        retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
        return(factor(retVal, levels = c("NA"
                ,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
                        ordered = TRUE))
    }
    , args = c("YOB"))

glbFeatsDerive[["Gender.fctr"]] <- list(
    mapfn = function(raw1) {
        raw <- raw1
        raw[raw %in% ""] <- "N"
        raw <- gsub("Male"  , "M", raw, fixed = TRUE)
        raw <- gsub("Female", "F", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("Gender"))

glbFeatsDerive[["Income.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("under $25,000"      , "<25K"    , raw, fixed = TRUE)
        raw <- gsub("$25,001 - $50,000"  , "25-50K"  , raw, fixed = TRUE)
        raw <- gsub("$50,000 - $74,999"  , "50-75K"  , raw, fixed = TRUE)
        raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)        
        raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
        raw <- gsub("over $150,000"      , ">150K"   , raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
                      ordered = TRUE))
    }
    , args = c("Income"))

glbFeatsDerive[["Hhold.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
        raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)        
        raw <- gsub("Married (no kids)"          , "MKn", raw, fixed = TRUE)
        raw <- gsub("Married (w/kids)"           , "MKy", raw, fixed = TRUE)        
        raw <- gsub("Single (no kids)"           , "SKn", raw, fixed = TRUE)
        raw <- gsub("Single (w/kids)"            , "SKy", raw, fixed = TRUE)        
        return(relevel(as.factor(raw), ref = "N"))
    }
    , args = c("HouseholdStatus"))

glbFeatsDerive[["Edn.fctr"]] <- list(
    mapfn = function(raw1) { raw <- raw1;
        raw[raw %in% ""] <- "N"
        raw <- gsub("Current K-12"         , "K12", raw, fixed = TRUE)
        raw <- gsub("High School Diploma"  , "HSD", raw, fixed = TRUE)        
        raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
        raw <- gsub("Associate's Degree"   , "Ast", raw, fixed = TRUE)
        raw <- gsub("Bachelor's Degree"    , "Bcr", raw, fixed = TRUE)        
        raw <- gsub("Master's Degree"      , "Msr", raw, fixed = TRUE)
        raw <- gsub("Doctoral Degree"      , "PhD", raw, fixed = TRUE)        
        return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
                      ordered = TRUE))
    }
    , args = c("EducationLevel"))

# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))    
    glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
        mapfn = function(raw1) {
            raw1[raw1 %in% ""] <- "NA"
            rawVal <- unique(raw1)
            
            if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
                raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
                raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
                raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
                raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
                raw1 <- gsub("Idealist"  , "Id", raw1, fixed = TRUE)
                raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            } else
            if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
                raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
                raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)                
                if (length(setdiff(rawVal, expVal)) > 0)
                    stop(qsn, " vals: ", paste0(rawVal, collapse = "|"), 
                         " does not match expectation: ", paste0(expVal, collapse = "|"))
            }
            
            return(relevel(as.factor(raw1), ref = "NA"))
        }
        , args = c(qsn))

# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
#     mapfn = function(FertilityRate, Region) {
#         RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
# 
#         retVal <- FertilityRate
#         retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
#         return(retVal)
#     }
#     , args = c("FertilityRate", "Region"))
    
#     mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }     
#     mapfn = function(Rasmussen)  { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) } 
#     mapfn = function(startprice) { return(startprice ^ (1/2)) }       
#     mapfn = function(startprice) { return(log(startprice)) }   
#     mapfn = function(startprice) { return(exp(-startprice / 20)) }
#     mapfn = function(startprice) { return(scale(log(startprice))) }     
#     mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }        

    # factor      
#     mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
#     mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
#     mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
#     mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5)); 
#                             tfr_raw[is.na(tfr_raw)] <- "NA.my";
#                             return(as.factor(tfr_raw)) }
#     mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
#     mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }    

#     , args = c("<arg1>"))
    
    # multiple args
#     mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }        
#     mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
#     mapfn = function(startprice.log10.predict, startprice) {
#                  return(spdiff <- (10 ^ startprice.log10.predict) - startprice) } 
#     mapfn = function(productline, description) { as.factor(
#         paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
#     mapfn = function(.src, .pos) { 
#         return(paste(.src, sprintf("%04d", 
#                                    ifelse(.src == "Train", .pos, .pos - 7049)
#                                    ), sep = "#")) }       

# # If glbObsAll is not sorted in the desired manner
#     mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
#     mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }

# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]

# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst))); 
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]); 

glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <- 
#     c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE, 
#       last.ctg = FALSE, poly.ctg = FALSE)

glbFeatsPrice <- NULL # or c("<price_var>")

glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation

glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
#   ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-screened-names>
#   ))))
#   ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL, 
#       <comma-separated-nonSCOWL-words>
#   ))))
#)

# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"

# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
    require(tm)
    require(stringr)

    glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
        # Remove any words from stopwords            
#         , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
                                
        # Remove salutations
        ,"mr","mrs","dr","Rev"                                

        # Remove misc
        #,"th" # Happy [[:digit::]]+th birthday 

        # Remove terms present in Trn only or New only; search for "Partition post-stem"
        #   ,<comma-separated-terms>        

        # cor.y.train == NA
#         ,unlist(strsplit(paste(c(NULL
#           ,"<comma-separated-terms>"
#         ), collapse=",")

        # freq == 1; keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>

        # nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
            # ,<comma-separated-terms>        
                                            )))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]

# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))

# To identify terms with a specific freq & 
#   are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")

#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]

# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))

# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)

# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])

# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")

# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]

# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Person names for names screening
#         ,<comma-separated-list>
#         
#         # Company names
#         ,<comma-separated-list>
#                     
#         # Product names
#         ,<comma-separated-list>
#     ))))

# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
#         # Words not in SCOWL db
#         ,<comma-separated-list>
#     ))))

# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)

# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
# 
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")

# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)

# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))

# Text Processing Step: mycombineSynonyms
#   To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
#   To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
#     cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
    print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
    print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
#     cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
#     cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl",  syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag",  syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent",  syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use",  syns=c("use", "usag")))

glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
#     # people in places
#     , list(word = "australia", syns = c("australia", "australian"))
#     , list(word = "italy", syns = c("italy", "Italian"))
#     , list(word = "newyork", syns = c("newyork", "newyorker"))    
#     , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))    
#     , list(word = "peru", syns = c("peru", "peruvian"))
#     , list(word = "qatar", syns = c("qatar", "qatari"))
#     , list(word = "scotland", syns = c("scotland", "scotish"))
#     , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))    
#     , list(word = "venezuela", syns = c("venezuela", "venezuelan"))    
# 
#     # companies - needs to be data dependent 
#     #   - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#         
#     # general synonyms
#     , list(word = "Create", syns = c("Create","Creator")) 
#     , list(word = "cute", syns = c("cute","cutest"))     
#     , list(word = "Disappear", syns = c("Disappear","Fadeout"))     
#     , list(word = "teach", syns = c("teach", "taught"))     
#     , list(word = "theater",  syns = c("theater", "theatre", "theatres")) 
#     , list(word = "understand",  syns = c("understand", "understood"))    
#     , list(word = "weak",  syns = c("weak", "weaken", "weaker", "weakest"))
#     , list(word = "wealth",  syns = c("wealth", "wealthi"))    
#     
#     # custom synonyms (phrases)
#     
#     # custom synonyms (names)
#                                       )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
#     , list(word="<stem1>",  syns=c("<stem1>", "<stem1_2>"))
#                                       )

for (txtFeat in names(glbFeatsTextSynonyms))
    for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
        glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
            str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)        
    }        

glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART 
glb_txt_terms_control <- list( # Gather model performance & run-time stats
                    # weighting = function(x) weightSMART(x, spec = "nnn")
                    # weighting = function(x) weightSMART(x, spec = "lnn")
                    # weighting = function(x) weightSMART(x, spec = "ann")
                    # weighting = function(x) weightSMART(x, spec = "bnn")
                    # weighting = function(x) weightSMART(x, spec = "Lnn")
                    # 
                    weighting = function(x) weightSMART(x, spec = "ltn") # default
                    # weighting = function(x) weightSMART(x, spec = "lpn")                    
                    # 
                    # weighting = function(x) weightSMART(x, spec = "ltc")                    
                    # 
                    # weighting = weightBin 
                    # weighting = weightTf 
                    # weighting = weightTfIdf # : default
                # termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
                    , bounds = list(global = c(1, Inf)) 
                # wordLengths selection criteria: tm default: c(3, Inf)
                    , wordLengths = c(1, Inf) 
                              ) 

glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)

# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq" 
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)

# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default 
names(glbFeatsTextAssocCor) <- names(glbFeatsText)

# Remember to use stemmed terms
glb_important_terms <- list()

# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")

# Have to set it even if it is not used
# Properties:
#   numrows(glb_feats_df) << numrows(glbObsFit
#   Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
#       numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)

glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer

glb_cluster <- FALSE # : default or TRUE
glb_cluster.seed <- 189 # or any integer
glb_cluster_entropy_var <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsTextClusterVarsExclude <- FALSE # default FALSE

glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")

glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default

glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258

glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
#     is.na(.rstudent)
#     max(.rstudent)
#     is.na(.dffits)
#     .hatvalues >= 0.99        
#     -38,167,642 < minmax(.rstudent) < 49,649,823    
#     , <comma-separated-<glbFeatsId>>
#                                     )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
                                c(NULL
                                ))

# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))

#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))

#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]])))); 
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)

#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]

# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)

# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()

# Add xgboost algorithm

# Regression
if (glb_is_regression) {
    glbMdlMethods <- c(NULL
        # deterministic
            #, "lm", # same as glm
            , "glm", "bayesglm", "glmnet"
            , "rpart"
        # non-deterministic
            , "gbm", "rf" 
        # Unknown
            , "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            , "bagEarth" # Takes a long time
            ,"xgbLinear","xgbTree"
        )
} else
# Classification - Add ada (auto feature selection)
    if (glb_is_binomial)
        glbMdlMethods <- c(NULL
        # deterministic                     
            , "bagEarth" # Takes a long time        
            , "glm", "bayesglm", "glmnet"
            , "nnet"
            , "rpart"
        # non-deterministic        
            , "gbm"
            , "avNNet" # runs 25 models per cv sample for tunelength=5      
            , "rf"
        # Unknown
            , "lda", "lda2"
                # svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
            , "svmLinear", "svmLinear2"
            , "svmPoly" # runs 75 models per cv sample for tunelength=5
            , "svmRadial" 
            , "earth"
            ,"xgbLinear","xgbTree"
        ) else
        glbMdlMethods <- c(NULL
        # deterministic
            ,"glmnet"
        # non-deterministic 
            ,"rf"       
        # Unknown
            ,"gbm","rpart","xgbLinear","xgbTree"
        )

glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
#   methods: Choose from c(NULL, <method>, glbMdlMethods) 
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
    # glm does not work for multinomial
    glbMdlFamilies[["All.X"]] <- c("glmnet") else    
    glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")

#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory

# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
#     , <comma-separated-features-vector>
#                                   )
# dAFeats.CSM.X %<d-% c(NULL
#     # Interaction feats up to varImp(RFE.X.glmnet) >= 50
#     , <comma-separated-features-vector>
#     , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
#                , <comma-separated-features-vector>
#                                                                       ))    
#                                   )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"

glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")

glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE

# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
                        ,data.frame(parameter = "alpha",  vals = "0.100 0.325 0.550 0.775 1.000")
                        ,data.frame(parameter = "lambda", vals = "9.342e-02")    
                        )
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
#                                cbind(data.frame(mdlId = "<mdlId>"),
#                                      glmnetTuneParams))

    #avNNet    
    #   size=[1] 3 5 7 9; decay=[0] 1e-04 0.001  0.01   0.1; bag=[FALSE]; RMSE=1.3300906 

    #bagEarth
    #   degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
#     ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")    
# ))

    #earth 
    #   degree=[1]; nprune=2  [9] 17 25 33; RMSE=0.1334478
    
    #gbm 
    #   shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313     
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
#     ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
#     ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
#     ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
#     #seq(from=0.05,  to=0.25, by=0.05)
# ))

    #glmnet
    #   alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
#     ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")    
# ))

    #nnet    
    #   size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
#     ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")    
# ))

    #rf # Don't bother; results are not deterministic
    #       mtry=2  35  68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))

    #rpart 
    #   cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()    
#     ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
    
    #svmLinear
    #   C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))

    #svmLinear2    
    #   cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354 
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))

    #svmPoly    
    #   degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
#     ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
#     ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
#     ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")    
# ))

    #svmRadial
    #   sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
    
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
    
glb_preproc_methods <- NULL
#     c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")

# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")

glbMdlMetric_terms <- NULL # or matrix(c(
#                               0,1,2,3,4,
#                               2,0,1,2,3,
#                               4,2,0,1,2,
#                               6,4,2,0,1,
#                               8,6,4,2,0
#                           ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression) 
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
#     confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
#     #print(confusion_mtrx)
#     #print(confusion_mtrx * glbMdlMetric_terms)
#     metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
#     names(metric) <- glbMdlMetricSummary
#     return(metric)
# }

glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL

glb_clf_proba_threshold <- NULL # 0.5

# Model selection criteria
if (glb_is_regression)
    glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
    #glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")    
if (glb_is_classification) {
    if (glb_is_binomial)
        glbMdlMetricsEval <- 
            c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else        
        glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}

# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
#     "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')" 
#     c(<comma-separated-mdlIds>
#      )

# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)

glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)

glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
#               List critical cols excl. above
                  )

# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
#     require(tidyr)
#     obsOutFinDf <- obsOutFinDf %>%
#         tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"), 
#                         sep = "#", remove = TRUE, extra = "merge")
#     # mnm prefix stands for max_n_mean
#     mnmout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         #dplyr::top_n(1, Probability1) %>% # Score = 3.9426         
#         #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;         
#         #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169; 
#         dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;        
#         #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#     
#         # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))    
#         # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
#         dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), 
#                          yMeanN = weighted.mean(as.numeric(y), c(Probability1)))  
#     
#     maxout_df <- obsOutFinDf %>%
#         dplyr::group_by(.pos) %>%
#         dplyr::summarize(maxProb1 = max(Probability1))
#     fltout_df <- merge(maxout_df, obsOutFinDf, 
#                        by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
#                        all.x = TRUE)
#     fmnout_df <- merge(fltout_df, mnmout_df, 
#                        by.x = c(".pos"), by.y = c(".pos"),
#                        all.x = TRUE)
#     return(fmnout_df)
# }
glbObsOut <- list(NULL
        # glbFeatsId will be the first output column, by default
        ,vars = list()
#         ,mapFn = function(obsOutFinDf) {
#                   }
                  )
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
#     txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
#         dplyr::mutate(
#             lunch     = levels(glbObsTrn[, "lunch"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "lunch"    ])), 0)],
#             dinner    = levels(glbObsTrn[, "dinner"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "dinner"   ])), 0)],
#             reserve   = levels(glbObsTrn[, "reserve"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "reserve"  ])), 0)],
#             outdoor   = levels(glbObsTrn[, "outdoor"  ])[
#                        round(mean(as.numeric(glbObsTrn[, "outdoor"  ])), 0)],
#             expensive = levels(glbObsTrn[, "expensive"])[
#                        round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
#             liquor    = levels(glbObsTrn[, "liquor"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "liquor"   ])), 0)],
#             table     = levels(glbObsTrn[, "table"    ])[
#                        round(mean(as.numeric(glbObsTrn[, "table"    ])), 0)],
#             classy    = levels(glbObsTrn[, "classy"   ])[
#                        round(mean(as.numeric(glbObsTrn[, "classy"   ])), 0)],
#             kids      = levels(glbObsTrn[, "kids"     ])[
#                        round(mean(as.numeric(glbObsTrn[, "kids"     ])), 0)]
#                       )
#     
#     print("ObsNew output class tables:")
#     print(sapply(c("lunch","dinner","reserve","outdoor",
#                    "expensive","liquor","table",
#                    "classy","kids"), 
#                  function(feat) table(txfout_df[, feat], useNA = "ifany")))
#     
#     txfout_df <- txfout_df %>%
#         dplyr::mutate(labels = "") %>%
#         dplyr::mutate(labels = 
#     ifelse(lunch     != "-1", paste(labels, lunch    ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(dinner    != "-1", paste(labels, dinner   ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(reserve   != "-1", paste(labels, reserve  ), labels)) %>%
#         dplyr::mutate(labels = 
#     ifelse(outdoor   != "-1", paste(labels, outdoor  ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(liquor    != "-1", paste(labels, liquor   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(table     != "-1", paste(labels, table    ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(classy    != "-1", paste(labels, classy   ), labels)) %>%
#         dplyr::mutate(labels =         
#     ifelse(kids      != "-1", paste(labels, kids     ), labels)) %>%
#         dplyr::select(business_id, labels)
#     return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))

glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")

if (glb_is_classification && glb_is_binomial) {
    # glbObsOut$vars[["Probability1"]] <- 
    #     "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]" 
    # glbObsOut$vars[[glb_rsp_var_raw]] <-
    #     "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
    #                                         mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
    glbObsOut$vars[["Predictions"]] <-
        "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
                                            mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
#     glbObsOut$vars[[glbFeatsId]] <- 
#         "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
    glbObsOut$vars[[glb_rsp_var]] <- 
        "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
#     for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
#         glbObsOut$vars[[outVar]] <- 
#             paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}    
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-

glbOutStackFnames <- NULL #: default
    # c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
    # c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack

glbOut <- list(pfx = "Votes_Q_02_cnk_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")


glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
    ,"import.data","inspect.data","scrub.data","transform.data"
    ,"extract.features"
        ,"extract.features.datetime","extract.features.image","extract.features.price"
        ,"extract.features.text","extract.features.string"  
        ,"extract.features.end"
    ,"manage.missing.data","cluster.data","partition.data.training","select.features"
    ,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
    ,"fit.data.training_0","fit.data.training_1"
    ,"predict.data.new"         
    ,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
    !identical(chkChunksLabels, glbChunks$labels)) {
    print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s", 
                  setdiff(chkChunksLabels, glbChunks$labels)))    
    print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s", 
                  setdiff(glbChunks$labels, chkChunksLabels)))    
}

glbChunks[["first"]] <- "cluster.data" #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #NULL #default: script will save envir at end of this chunk 
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk

# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])

#load("Votes_Q_02_cnk_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))

# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
                        trans_df = data.frame(id = 1:6,
    name = c("data.training.all","data.new",
           "model.selected","model.final",
           "data.training.all.prediction","data.new.prediction"),
    x=c(   -5,-5,-15,-25,-25,-35),
    y=c(   -5, 5,  0,  0, -5,  5)
                        ),
                        places_df=data.frame(id=1:4,
    name=c("bgn","fit.data.training.all","predict.data.new","end"),
    x=c(   -0,   -20,                    -30,               -40),
    y=c(    0,     0,                      0,                 0),
    M0=c(   3,     0,                      0,                 0)
                        ),
                        arcs_df = data.frame(
    begin = c("bgn","bgn","bgn",        
            "data.training.all","model.selected","fit.data.training.all",
            "fit.data.training.all","model.final",    
            "data.new","predict.data.new",
            "data.training.all.prediction","data.new.prediction"),
    end   = c("data.training.all","data.new","model.selected",
            "fit.data.training.all","fit.data.training.all","model.final",
            "data.training.all.prediction","predict.data.new",
            "predict.data.new","data.new.prediction",
            "end","end")
                        ))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid

glb_analytics_avl_objs <- NULL

glb_chunks_df <- myadd_chunk(NULL, 
                             ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))
##          label step_major step_minor label_minor  bgn end elapsed
## 1 cluster.data          1          0           0 8.05  NA      NA

Step 1.0: cluster data

chunk option: eval=

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

```{r extract.features.image, cache=FALSE, echo=FALSE, fig.height=5, fig.width=5, eval=myevlChunk(glbChunks, glbOut$pfx)}

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

Step 1.0: cluster data

```{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)}

##                     label step_major step_minor label_minor   bgn   end
## 1            cluster.data          1          0           0 8.050 9.292
## 2 partition.data.training          2          0           0 9.292    NA
##   elapsed
## 1   1.242
## 2      NA

Step 2.0: partition data training

## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
## Loading required package: reshape2
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 1.08 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 1.08 secs"
## Loading required package: sampling
## 
## Attaching package: 'sampling'
## The following object is masked from 'package:caret':
## 
##     cluster
## [1] "lclgetMatrixCorrelation: duration: 40.428000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 15.229000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 50.311000 secs"
## [1] "cor of Fit vs. New: 1.0000"
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 108.10 secs"
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA     1392
## Fit           2357             2091       NA
## OOB            594              526       NA
##     Party.Democrat Party.Republican Party.NA
##                 NA               NA        1
## Fit      0.5299011        0.4700989       NA
## OOB      0.5303571        0.4696429       NA
##   Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6        SKn   1920    511    638     0.43165468    0.456250000
## 2        MKy   1296    298    371     0.29136691    0.266071429
## 1        MKn    516    136    169     0.11600719    0.121428571
## 3          N    367     83    102     0.08250899    0.074107143
## 7        SKy    147     53     65     0.03304856    0.047321429
## 4        PKn    150     30     37     0.03372302    0.026785714
## 5        PKy     52      9     10     0.01169065    0.008035714
##   .freqRatio.Tst
## 6    0.458333333
## 2    0.266522989
## 1    0.121408046
## 3    0.073275862
## 7    0.046695402
## 4    0.026580460
## 5    0.007183908
## [1] "glbObsAll: "
## [1] 6960  219
## [1] "glbObsTrn: "
## [1] 5568  219
## [1] "glbObsFit: "
## [1] 4448  218
## [1] "glbObsOOB: "
## [1] 1120  218
## [1] "glbObsNew: "
## [1] 1392  218
## [1] "partition.data.training chunk: teardown: elapsed: 109.04 secs"
##                     label step_major step_minor label_minor     bgn
## 2 partition.data.training          2          0           0   9.292
## 3         select.features          3          0           0 118.368
##       end elapsed
## 2 118.368 109.076
## 3      NA      NA

Step 3.0: select features

## [1] "cor(Q98059.fctr, Q98078.fctr)=0.7689"
## [1] "cor(Party.fctr, Q98059.fctr)=0.0172"
## [1] "cor(Party.fctr, Q98078.fctr)=0.0257"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98059.fctr as highly correlated with Q98078.fctr
## [1] "cor(Q99480.fctr, Q99581.fctr)=0.7660"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"
## [1] "cor(Party.fctr, Q99581.fctr)=-0.0104"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99581.fctr as highly correlated with Q99480.fctr
## [1] "cor(Q108855.fctr, Q108856.fctr)=0.7430"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"
## [1] "cor(Party.fctr, Q108856.fctr)=-0.0140"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108856.fctr as highly correlated with Q108855.fctr
## [1] "cor(Q122770.fctr, Q122771.fctr)=0.7379"
## [1] "cor(Party.fctr, Q122770.fctr)=-0.0195"
## [1] "cor(Party.fctr, Q122771.fctr)=-0.0348"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q122770.fctr as highly correlated with Q122771.fctr
## [1] "cor(Q106272.fctr, Q106388.fctr)=0.7339"
## [1] "cor(Party.fctr, Q106272.fctr)=-0.0401"
## [1] "cor(Party.fctr, Q106388.fctr)=-0.0342"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q106388.fctr as highly correlated with Q106272.fctr
## [1] "cor(Q100680.fctr, Q100689.fctr)=0.7292"
## [1] "cor(Party.fctr, Q100680.fctr)=0.0158"
## [1] "cor(Party.fctr, Q100689.fctr)=0.0257"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q100680.fctr as highly correlated with Q100689.fctr
## [1] "cor(Q99480.fctr, Q99716.fctr)=0.7252"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"
## [1] "cor(Party.fctr, Q99716.fctr)=0.0209"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q99716.fctr as highly correlated with Q99480.fctr
## [1] "cor(Q120472.fctr, Q120650.fctr)=0.7126"
## [1] "cor(Party.fctr, Q120472.fctr)=-0.0462"
## [1] "cor(Party.fctr, Q120650.fctr)=-0.0271"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q120650.fctr as highly correlated with Q120472.fctr
## [1] "cor(Q98869.fctr, Q99480.fctr)=0.7084"
## [1] "cor(Party.fctr, Q98869.fctr)=-0.0277"
## [1] "cor(Party.fctr, Q99480.fctr)=-0.0344"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q98869.fctr as highly correlated with Q99480.fctr
## [1] "cor(Q123464.fctr, Q123621.fctr)=0.7078"
## [1] "cor(Party.fctr, Q123464.fctr)=-0.0136"
## [1] "cor(Party.fctr, Q123621.fctr)=-0.0255"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q123464.fctr as highly correlated with Q123621.fctr
## [1] "cor(Q108754.fctr, Q108855.fctr)=0.7005"
## [1] "cor(Party.fctr, Q108754.fctr)=-0.0081"
## [1] "cor(Party.fctr, Q108855.fctr)=-0.0371"
## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glbObsTrn, : Identified Q108754.fctr as highly correlated with Q108855.fctr
##                      cor.y exclude.as.feat    cor.y.abs   cor.high.X
## Q109244.fctr  0.1203812469               0 0.1203812469         <NA>
## Hhold.fctr    0.0511386673               0 0.0511386673         <NA>
## Edn.fctr      0.0359295351               0 0.0359295351         <NA>
## Q101163.fctr  0.0295046473               0 0.0295046473         <NA>
## Q100689.fctr  0.0256915080               0 0.0256915080         <NA>
## Q98078.fctr   0.0256516490               0 0.0256516490         <NA>
## Q99716.fctr   0.0209286674               0 0.0209286674  Q99480.fctr
## Q120379.fctr  0.0206291292               0 0.0206291292         <NA>
## Q121699.fctr  0.0196933075               0 0.0196933075         <NA>
## Q105840.fctr  0.0195569165               0 0.0195569165         <NA>
## Q113583.fctr  0.0191894717               0 0.0191894717         <NA>
## Q115195.fctr  0.0174522586               0 0.0174522586         <NA>
## Q102089.fctr  0.0174087944               0 0.0174087944         <NA>
## Q98059.fctr   0.0171637755               0 0.0171637755  Q98078.fctr
## Q114386.fctr  0.0168013326               0 0.0168013326         <NA>
## Q100680.fctr  0.0157762454               0 0.0157762454 Q100689.fctr
## Q108342.fctr  0.0151842510               0 0.0151842510         <NA>
## Q111848.fctr  0.0141099384               0 0.0141099384         <NA>
## YOB.Age.fctr  0.0129198495               0 0.0129198495         <NA>
## Q118892.fctr  0.0125250379               0 0.0125250379         <NA>
## Q102687.fctr  0.0120079165               0 0.0120079165         <NA>
## Q115390.fctr  0.0119300319               0 0.0119300319         <NA>
## Q119851.fctr  0.0093381833               0 0.0093381833         <NA>
## Q114517.fctr  0.0084741753               0 0.0084741753         <NA>
## Q120012.fctr  0.0084652930               0 0.0084652930         <NA>
## Q109367.fctr  0.0080456026               0 0.0080456026         <NA>
## Q114961.fctr  0.0079206587               0 0.0079206587         <NA>
## Q121700.fctr  0.0067756198               0 0.0067756198         <NA>
## Q124122.fctr  0.0061257448               0 0.0061257448         <NA>
## Q111220.fctr  0.0055758571               0 0.0055758571         <NA>
## Q113992.fctr  0.0041479796               0 0.0041479796         <NA>
## Q121011.fctr  0.0037329030               0 0.0037329030         <NA>
## Q106042.fctr  0.0032327194               0 0.0032327194         <NA>
## Q116448.fctr  0.0031731051               0 0.0031731051         <NA>
## Q116601.fctr  0.0022379241               0 0.0022379241         <NA>
## Q104996.fctr  0.0012202806               0 0.0012202806         <NA>
## Q102906.fctr  0.0011540297               0 0.0011540297         <NA>
## Q113584.fctr  0.0011387024               0 0.0011387024         <NA>
## Q108950.fctr  0.0010567028               0 0.0010567028         <NA>
## Q102674.fctr  0.0009759844               0 0.0009759844         <NA>
## Q103293.fctr  0.0005915534               0 0.0005915534         <NA>
## Q112478.fctr  0.0001517248               0 0.0001517248         <NA>
## Q114748.fctr -0.0008477228               0 0.0008477228         <NA>
## Q107491.fctr -0.0014031814               0 0.0014031814         <NA>
## Q100562.fctr -0.0017132769               0 0.0017132769         <NA>
## Q108617.fctr -0.0024119725               0 0.0024119725         <NA>
## Q100010.fctr -0.0024291540               0 0.0024291540         <NA>
## Q115602.fctr -0.0027844465               0 0.0027844465         <NA>
## Q116953.fctr -0.0029786716               0 0.0029786716         <NA>
## Q115610.fctr -0.0035255582               0 0.0035255582         <NA>
## Q106997.fctr -0.0041749086               0 0.0041749086         <NA>
## Q120978.fctr -0.0044187616               0 0.0044187616         <NA>
## Q112512.fctr -0.0056768212               0 0.0056768212         <NA>
## Q108343.fctr -0.0060665340               0 0.0060665340         <NA>
## Q96024.fctr  -0.0069116541               0 0.0069116541         <NA>
## Q106389.fctr -0.0077498918               0 0.0077498918         <NA>
## .rnorm       -0.0078039520               0 0.0078039520         <NA>
## Q108754.fctr -0.0080847764               0 0.0080847764 Q108855.fctr
## Q98578.fctr  -0.0081164509               0 0.0081164509         <NA>
## Q101162.fctr -0.0099412952               0 0.0099412952         <NA>
## Q115777.fctr -0.0101315203               0 0.0101315203         <NA>
## Q99581.fctr  -0.0103662478               0 0.0103662478  Q99480.fctr
## Q124742.fctr -0.0111642906               0 0.0111642906         <NA>
## Q116797.fctr -0.0112749656               0 0.0112749656         <NA>
## Q112270.fctr -0.0116157798               0 0.0116157798         <NA>
## YOB          -0.0116828198               1 0.0116828198         <NA>
## Q118237.fctr -0.0117079669               0 0.0117079669         <NA>
## Q119650.fctr -0.0125645475               0 0.0125645475         <NA>
## Q111580.fctr -0.0132382335               0 0.0132382335         <NA>
## Q123464.fctr -0.0136140083               0 0.0136140083 Q123621.fctr
## Q117193.fctr -0.0138241599               0 0.0138241599         <NA>
## Q99982.fctr  -0.0139727928               0 0.0139727928         <NA>
## Q108856.fctr -0.0140363785               0 0.0140363785 Q108855.fctr
## Q118233.fctr -0.0147269325               0 0.0147269325         <NA>
## Q102289.fctr -0.0155850393               0 0.0155850393         <NA>
## Q116197.fctr -0.0158561766               0 0.0158561766         <NA>
## Income.fctr  -0.0159635458               0 0.0159635458         <NA>
## Q118232.fctr -0.0171321152               0 0.0171321152         <NA>
## Q120194.fctr -0.0172986920               0 0.0172986920         <NA>
## Q114152.fctr -0.0175013163               0 0.0175013163         <NA>
## Q122770.fctr -0.0194639697               0 0.0194639697 Q122771.fctr
## Q117186.fctr -0.0198853672               0 0.0198853672         <NA>
## Q105655.fctr -0.0198994078               0 0.0198994078         <NA>
## Q106993.fctr -0.0207428635               0 0.0207428635         <NA>
## Q119334.fctr -0.0226894034               0 0.0226894034         <NA>
## Q122120.fctr -0.0229287700               0 0.0229287700         <NA>
## Q116441.fctr -0.0237358205               0 0.0237358205         <NA>
## Q118117.fctr -0.0253544150               0 0.0253544150         <NA>
## Q123621.fctr -0.0255329743               0 0.0255329743         <NA>
## Q122769.fctr -0.0259739146               0 0.0259739146         <NA>
## Q120650.fctr -0.0270889067               0 0.0270889067 Q120472.fctr
## Q98869.fctr  -0.0276734114               0 0.0276734114  Q99480.fctr
## .pos         -0.0302037138               1 0.0302037138         <NA>
## USER_ID      -0.0302304868               1 0.0302304868         <NA>
## Q107869.fctr -0.0304661021               0 0.0304661021         <NA>
## Q120014.fctr -0.0318620439               0 0.0318620439         <NA>
## Q115899.fctr -0.0324177950               0 0.0324177950         <NA>
## Q106388.fctr -0.0341579350               0 0.0341579350 Q106272.fctr
## Q99480.fctr  -0.0344412239               0 0.0344412239         <NA>
## Q122771.fctr -0.0348421015               0 0.0348421015         <NA>
## Q108855.fctr -0.0370970211               0 0.0370970211         <NA>
## Q110740.fctr -0.0380691243               0 0.0380691243         <NA>
## Q106272.fctr -0.0400926462               0 0.0400926462         <NA>
## Q101596.fctr -0.0409784077               0 0.0409784077         <NA>
## Q116881.fctr -0.0416860293               0 0.0416860293         <NA>
## Q120472.fctr -0.0462030674               0 0.0462030674         <NA>
## Q98197.fctr  -0.0549342527               0 0.0549342527         <NA>
## Q113181.fctr -0.0808753072               0 0.0808753072         <NA>
## Q115611.fctr -0.0904468203               0 0.0904468203         <NA>
## Gender.fctr  -0.1027400851               0 0.1027400851         <NA>
##              freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## Q109244.fctr  1.125916    0.05387931   FALSE FALSE            FALSE
## Hhold.fctr    1.525094    0.12571839   FALSE FALSE            FALSE
## Edn.fctr      1.392610    0.14367816   FALSE FALSE            FALSE
## Q101163.fctr  1.327394    0.05387931   FALSE FALSE            FALSE
## Q100689.fctr  1.029800    0.05387931   FALSE FALSE            FALSE
## Q98078.fctr   1.266595    0.05387931   FALSE FALSE            FALSE
## Q99716.fctr   1.328693    0.05387931   FALSE FALSE            FALSE
## Q120379.fctr  1.046326    0.05387931   FALSE FALSE            FALSE
## Q121699.fctr  1.507127    0.05387931   FALSE FALSE            FALSE
## Q105840.fctr  1.275362    0.05387931   FALSE FALSE            FALSE
## Q113583.fctr  1.102515    0.05387931   FALSE FALSE            FALSE
## Q115195.fctr  1.065496    0.05387931   FALSE FALSE            FALSE
## Q102089.fctr  1.055963    0.05387931   FALSE FALSE            FALSE
## Q98059.fctr   1.493810    0.05387931   FALSE FALSE            FALSE
## Q114386.fctr  1.092072    0.05387931   FALSE FALSE            FALSE
## Q100680.fctr  1.102386    0.05387931   FALSE FALSE            FALSE
## Q108342.fctr  1.048292    0.05387931   FALSE FALSE            FALSE
## Q111848.fctr  1.113602    0.05387931   FALSE FALSE            FALSE
## YOB.Age.fctr  1.005794    0.16163793   FALSE FALSE            FALSE
## Q118892.fctr  1.347380    0.05387931   FALSE FALSE            FALSE
## Q102687.fctr  1.256545    0.05387931   FALSE FALSE            FALSE
## Q115390.fctr  1.150505    0.05387931   FALSE FALSE            FALSE
## Q119851.fctr  1.244519    0.05387931   FALSE FALSE            FALSE
## Q114517.fctr  1.183374    0.05387931   FALSE FALSE            FALSE
## Q120012.fctr  1.047185    0.05387931   FALSE FALSE            FALSE
## Q109367.fctr  1.008571    0.05387931   FALSE FALSE            FALSE
## Q114961.fctr  1.250436    0.05387931   FALSE FALSE            FALSE
## Q121700.fctr  1.708221    0.05387931   FALSE FALSE             TRUE
## Q124122.fctr  1.412807    0.05387931   FALSE FALSE             TRUE
## Q111220.fctr  1.262849    0.05387931   FALSE FALSE             TRUE
## Q113992.fctr  1.267442    0.05387931   FALSE FALSE             TRUE
## Q121011.fctr  1.153676    0.05387931   FALSE FALSE             TRUE
## Q106042.fctr  1.247738    0.05387931   FALSE FALSE             TRUE
## Q116448.fctr  1.161031    0.05387931   FALSE FALSE             TRUE
## Q116601.fctr  1.394914    0.05387931   FALSE FALSE             TRUE
## Q104996.fctr  1.173840    0.05387931   FALSE FALSE             TRUE
## Q102906.fctr  1.053396    0.05387931   FALSE FALSE             TRUE
## Q113584.fctr  1.212486    0.05387931   FALSE FALSE             TRUE
## Q108950.fctr  1.103872    0.05387931   FALSE FALSE             TRUE
## Q102674.fctr  1.073412    0.05387931   FALSE FALSE             TRUE
## Q103293.fctr  1.122287    0.05387931   FALSE FALSE             TRUE
## Q112478.fctr  1.113648    0.05387931   FALSE FALSE             TRUE
## Q114748.fctr  1.051125    0.05387931   FALSE FALSE             TRUE
## Q107491.fctr  1.419021    0.05387931   FALSE FALSE             TRUE
## Q100562.fctr  1.217215    0.05387931   FALSE FALSE             TRUE
## Q108617.fctr  1.390618    0.05387931   FALSE FALSE             TRUE
## Q100010.fctr  1.268156    0.05387931   FALSE FALSE             TRUE
## Q115602.fctr  1.322302    0.05387931   FALSE FALSE             TRUE
## Q116953.fctr  1.039180    0.05387931   FALSE FALSE             TRUE
## Q115610.fctr  1.359695    0.05387931   FALSE FALSE             TRUE
## Q106997.fctr  1.177632    0.05387931   FALSE FALSE             TRUE
## Q120978.fctr  1.131963    0.05387931   FALSE FALSE             TRUE
## Q112512.fctr  1.299253    0.05387931   FALSE FALSE             TRUE
## Q108343.fctr  1.064910    0.05387931   FALSE FALSE             TRUE
## Q96024.fctr   1.144428    0.05387931   FALSE FALSE             TRUE
## Q106389.fctr  1.341307    0.05387931   FALSE FALSE             TRUE
## .rnorm        1.000000  100.00000000   FALSE FALSE            FALSE
## Q108754.fctr  1.008090    0.05387931   FALSE FALSE            FALSE
## Q98578.fctr   1.093556    0.05387931   FALSE FALSE            FALSE
## Q101162.fctr  1.103229    0.05387931   FALSE FALSE            FALSE
## Q115777.fctr  1.140288    0.05387931   FALSE FALSE            FALSE
## Q99581.fctr   1.375000    0.05387931   FALSE FALSE            FALSE
## Q124742.fctr  2.565379    0.05387931   FALSE FALSE            FALSE
## Q116797.fctr  1.009589    0.05387931   FALSE FALSE            FALSE
## Q112270.fctr  1.254284    0.05387931   FALSE FALSE            FALSE
## YOB           1.027559    1.41882184   FALSE FALSE            FALSE
## Q118237.fctr  1.088017    0.05387931   FALSE FALSE            FALSE
## Q119650.fctr  1.456978    0.05387931   FALSE FALSE            FALSE
## Q111580.fctr  1.024977    0.05387931   FALSE FALSE            FALSE
## Q123464.fctr  1.326681    0.05387931   FALSE FALSE            FALSE
## Q117193.fctr  1.140665    0.05387931   FALSE FALSE            FALSE
## Q99982.fctr   1.339380    0.05387931   FALSE FALSE            FALSE
## Q108856.fctr  1.080645    0.05387931   FALSE FALSE            FALSE
## Q118233.fctr  1.199142    0.05387931   FALSE FALSE            FALSE
## Q102289.fctr  1.033482    0.05387931   FALSE FALSE            FALSE
## Q116197.fctr  1.073778    0.05387931   FALSE FALSE            FALSE
## Income.fctr   1.256724    0.12571839   FALSE FALSE            FALSE
## Q118232.fctr  1.365812    0.05387931   FALSE FALSE            FALSE
## Q120194.fctr  1.016716    0.05387931   FALSE FALSE            FALSE
## Q114152.fctr  1.027617    0.05387931   FALSE FALSE            FALSE
## Q122770.fctr  1.008802    0.05387931   FALSE FALSE            FALSE
## Q117186.fctr  1.053878    0.05387931   FALSE FALSE            FALSE
## Q105655.fctr  1.079316    0.05387931   FALSE FALSE            FALSE
## Q106993.fctr  1.327392    0.05387931   FALSE FALSE            FALSE
## Q119334.fctr  1.081498    0.05387931   FALSE FALSE            FALSE
## Q122120.fctr  1.297443    0.05387931   FALSE FALSE            FALSE
## Q116441.fctr  1.019645    0.05387931   FALSE FALSE            FALSE
## Q118117.fctr  1.174006    0.05387931   FALSE FALSE            FALSE
## Q123621.fctr  1.466381    0.05387931   FALSE FALSE            FALSE
## Q122769.fctr  1.060606    0.05387931   FALSE FALSE            FALSE
## Q120650.fctr  1.896247    0.05387931   FALSE FALSE            FALSE
## Q98869.fctr   1.080860    0.05387931   FALSE FALSE            FALSE
## .pos          1.000000  100.00000000   FALSE FALSE            FALSE
## USER_ID       1.000000  100.00000000   FALSE FALSE            FALSE
## Q107869.fctr  1.211050    0.05387931   FALSE FALSE            FALSE
## Q120014.fctr  1.044944    0.05387931   FALSE FALSE            FALSE
## Q115899.fctr  1.197849    0.05387931   FALSE FALSE            FALSE
## Q106388.fctr  1.065033    0.05387931   FALSE FALSE            FALSE
## Q99480.fctr   1.225404    0.05387931   FALSE FALSE            FALSE
## Q122771.fctr  1.414753    0.05387931   FALSE FALSE            FALSE
## Q108855.fctr  1.273980    0.05387931   FALSE FALSE            FALSE
## Q110740.fctr  1.050779    0.05387931   FALSE FALSE            FALSE
## Q106272.fctr  1.116536    0.05387931   FALSE FALSE            FALSE
## Q101596.fctr  1.041667    0.05387931   FALSE FALSE            FALSE
## Q116881.fctr  1.010066    0.05387931   FALSE FALSE            FALSE
## Q120472.fctr  1.292633    0.05387931   FALSE FALSE            FALSE
## Q98197.fctr   1.129371    0.05387931   FALSE FALSE            FALSE
## Q113181.fctr  1.006354    0.05387931   FALSE FALSE            FALSE
## Q115611.fctr  1.194859    0.05387931   FALSE FALSE            FALSE
## Gender.fctr   1.561033    0.05387931   FALSE FALSE            FALSE
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## [1] cor.y            exclude.as.feat  cor.y.abs        cor.high.X      
## [5] freqRatio        percentUnique    zeroVar          nzv             
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.

## [1] "numeric data missing in glbObsAll: "
##        YOB Party.fctr 
##        415       1392 
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
##          Gender          Income HouseholdStatus  EducationLevel 
##             143            1273             552            1067 
##           Party         Q124742         Q124122         Q123464 
##              NA            4340            3114            2912 
##         Q123621         Q122769         Q122770         Q122771 
##            3018            2778            2597            2579 
##         Q122120         Q121699         Q121700         Q120978 
##            2552            2279            2328            2303 
##         Q121011         Q120379         Q120650         Q120472 
##            2256            2361            2283            2433 
##         Q120194         Q120012         Q120014         Q119334 
##            2603            2344            2571            2477 
##         Q119851         Q119650         Q118892         Q118117 
##            2243            2374            2206            2342 
##         Q118232         Q118233         Q118237         Q117186 
##            3018            2659            2592            2845 
##         Q117193         Q116797         Q116881         Q116953 
##            2799            2771            2889            2848 
##         Q116601         Q116441         Q116448         Q116197 
##            2606            2684            2730            2657 
##         Q115602         Q115777         Q115610         Q115611 
##            2619            2785            2637            2443 
##         Q115899         Q115390         Q114961         Q114748 
##            2789            2860            2687            2462 
##         Q115195         Q114517         Q114386         Q113992 
##            2647            2567            2686            2502 
##         Q114152         Q113583         Q113584         Q113181 
##            2829            2632            2654            2576 
##         Q112478         Q112512         Q112270         Q111848 
##            2790            2676            2820            2449 
##         Q111580         Q111220         Q110740         Q109367 
##            2686            2563            2479            2624 
##         Q108950         Q109244         Q108855         Q108617 
##            2641            2731            3008            2696 
##         Q108856         Q108754         Q108342         Q108343 
##            3007            2770            2760            2736 
##         Q107869         Q107491         Q106993         Q106997 
##            2762            2667            2676            2702 
##         Q106272         Q106388         Q106389         Q106042 
##            2722            2818            2871            2762 
##         Q105840         Q105655         Q104996         Q103293 
##            2876            2612            2620            2674 
##         Q102906         Q102674         Q102687         Q102289 
##            2840            2864            2712            2790 
##         Q102089         Q101162         Q101163         Q101596 
##            2736            2816            2995            2824 
##         Q100689         Q100680         Q100562          Q99982 
##            2568            2787            2793            2871 
##         Q100010          Q99716          Q99581          Q99480 
##            2688            2790            2690            2700 
##          Q98869          Q98578          Q98059          Q98078 
##            2906            2867            2629            2945 
##          Q98197          Q96024            .lcn 
##            2836            2858            1392
## [1] "glb_feats_df:"
## [1] 110  12
##                    id exclude.as.feat rsp_var
## Party.fctr Party.fctr            TRUE    TRUE
##                    id       cor.y exclude.as.feat  cor.y.abs cor.high.X
## USER_ID       USER_ID -0.03023049            TRUE 0.03023049       <NA>
## Party.fctr Party.fctr          NA            TRUE         NA       <NA>
##            freqRatio percentUnique zeroVar   nzv is.cor.y.abs.low
## USER_ID            1           100   FALSE FALSE            FALSE
## Party.fctr        NA            NA      NA    NA               NA
##            interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID                  NA                   NA       FALSE   TRUE
## Party.fctr               NA                   NA          NA     NA
##            rsp_var
## USER_ID         NA
## Party.fctr    TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
##             label step_major step_minor label_minor     bgn     end
## 3 select.features          3          0           0 118.368 124.554
## 4      fit.models          4          0           0 124.554      NA
##   elapsed
## 3   6.186
## 4      NA

Step 4.0: fit models

fit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_0_bgn          1          0       setup 125.075  NA      NA
# load(paste0(glbOut$pfx, "dsk.RData"))

get_model_sel_frmla <- function() {
    model_evl_terms <- c(NULL)
    # min.aic.fit might not be avl
    lclMdlEvlCriteria <- 
        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
    for (metric in lclMdlEvlCriteria)
        model_evl_terms <- c(model_evl_terms, 
                             ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
    if (glb_is_classification && glb_is_binomial)
        model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
    model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
    return(model_sel_frmla)
}

get_dsp_models_df <- function() {
    dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    dsp_models_df <- 
        #orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
        orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]    
    nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
    nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0, 
        nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
    
#     nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
#     nParams <- nParams[names(nParams) != "avNNet"]    
    
    if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
        print("Cross Validation issues:")
        warning("Cross Validation issues:")        
        print(cvMdlProblems)
    }
    
    pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
    pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
    
    # length(pltMdls) == 21
    png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
    pltIx <- 1
    for (mdlId in pltMdls) {
        print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),   
              vp = viewport(layout.pos.row = ceiling(pltIx / 2.0), 
                            layout.pos.col = ((pltIx - 1) %% 2) + 1))  
        pltIx <- pltIx + 1
    }
    dev.off()

    if (all(row.names(dsp_models_df) != dsp_models_df$id))
        row.names(dsp_models_df) <- dsp_models_df$id
    return(dsp_models_df)
}
#get_dsp_models_df()

if (glb_is_classification && glb_is_binomial && 
        (length(unique(glbObsFit[, glb_rsp_var])) < 2))
    stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
         paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))

max_cor_y_x_vars <- orderBy(~ -cor.y.abs, 
        subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low & 
                                is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
    max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")

if (!is.null(glb_Baseline_mdl_var)) {
    if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) & 
        (glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] > 
         glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
        stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var, 
             " than the Baseline var: ", glb_Baseline_mdl_var)
}

glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
    
# Model specs
# c("id.prefix", "method", "type",
#   # trainControl params
#   "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
#   # train params
#   "metric", "metric.maximize", "tune.df")

# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                            paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
                                    label.minor = "mybaseln_classfr")
    ret_lst <- myfit_mdl(mdl_id="Baseline", 
                         model_method="mybaseln_classfr",
                        indepVar=glb_Baseline_mdl_var,
                        rsp_var=glb_rsp_var,
                        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    

# Most Frequent Outcome "MFO" model: mean(y) for regression
#   Not using caret's nullModel since model stats not avl
#   Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "MFO"), major.inc = FALSE,
                                        label.minor = "myMFO_classfr")

    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
        train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
                            indepVar = ".rnorm", rsp_var = glb_rsp_var,
                            fit_df = glbObsFit, OOB_df = glbObsOOB)

        # "random" model - only for classification; 
        #   none needed for regression since it is same as MFO
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                                paste0("fit.models_0_", "Random"), major.inc = FALSE,
                                        label.minor = "myrandom_classfr")

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)    
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
        train.method = "myrandom_classfr")),
                        indepVar = ".rnorm", rsp_var = glb_rsp_var,
                        fit_df = glbObsFit, OOB_df = glbObsOOB)
}
##              label step_major step_minor   label_minor     bgn     end
## 1 fit.models_0_bgn          1          0         setup 125.075 125.108
## 2 fit.models_0_MFO          1          1 myMFO_classfr 125.108      NA
##   elapsed
## 1   0.033
## 2      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.441000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] R D
## Levels: R D
## [1] "unique.prob:"
## y
##         D         R 
## 0.5299011 0.4700989 
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.836000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      -none-     numeric  
## MFO.val     1      -none-     character
## x.names     1      -none-     character
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.838000 secs"
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
## 
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
## 
##     lowess
## [1] "in MFO.Classifier$prob"
##           R         D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000 
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
##           R         D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 5.670000 secs"
##                    id  feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm               0                      0.386
##   min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1                 0.003             0.5            0            1
##   max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1             0.5                    0.5       0.6395473        0.4700989
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.4553427             0.4848945             0
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1             0.5            0            1             0.5
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                    0.5       0.6391252        0.4696429
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.4400805             0.4993651             0
## [1] "myfit_mdl: exit: 5.679000 secs"
##                 label step_major step_minor      label_minor     bgn
## 2    fit.models_0_MFO          1          1    myMFO_classfr 125.108
## 3 fit.models_0_Random          1          2 myrandom_classfr 130.793
##       end elapsed
## 2 130.793   5.685
## 3      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] "    indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.432000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.709000 secs"
##             Length Class      Mode     
## unique.vals 2      factor     numeric  
## unique.prob 2      table      numeric  
## xNames      1      -none-     character
## problemType 1      -none-     character
## tuneValue   1      data.frame list     
## obsLevels   2      -none-     character
## [1] "myfit_mdl: train diagnostics complete: 0.710000 secs"
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference    R    D
##         R 2091    0
##         D 2357    0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##      0.4700989      0.0000000      0.4553427      0.4848945      0.5299011 
## AccuracyPValue  McnemarPValue 
##      1.0000000      0.0000000 
## [1] "in Random.Classifier$prob"

##          Prediction
## Reference   R   D
##         R 526   0
##         D 594   0
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   4.696429e-01   0.000000e+00   4.400805e-01   4.993651e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   9.999790e-01  9.194240e-131 
## [1] "myfit_mdl: predict complete: 6.510000 secs"
##                          id  feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                       0.27                 0.002       0.4942483
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.4619799    0.5265168       0.5073101                   0.55
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6395473        0.4700989             0.4553427
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.4848945             0        0.523569          0.5
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1     0.547138       0.5191202                   0.55       0.6391252
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.4696429             0.4400805             0.4993651
##   max.Kappa.OOB
## 1             0
## [1] "myfit_mdl: exit: 6.523000 secs"
# Max.cor.Y
#   Check impact of cv
#       rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
                                    label.minor = "glmnet")
##                            label step_major step_minor      label_minor
## 3            fit.models_0_Random          1          2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X*          1          3           glmnet
##       bgn     end elapsed
## 3 130.793 137.327   6.534
## 4 137.327      NA      NA
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
    id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
    train.method = "glmnet")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] "    indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.700000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
## Loaded glmnet 2.0-5
## Fitting alpha = 0.1, lambda = 0.00248 on full training set
## [1] "myfit_mdl: train complete: 1.500000 secs"

##             Length Class      Mode     
## a0           58    -none-     numeric  
## beta        232    dgCMatrix  S4       
## df           58    -none-     numeric  
## dim           2    -none-     numeric  
## lambda       58    -none-     numeric  
## dev.ratio    58    -none-     numeric  
## nulldev       1    -none-     numeric  
## npasses       1    -none-     numeric  
## jerr          1    -none-     numeric  
## offset        1    -none-     logical  
## classnames    2    -none-     character
## call          5    -none-     call     
## nobs          1    -none-     numeric  
## lambdaOpt     1    -none-     numeric  
## xNames        4    -none-     character
## problemType   1    -none-     character
## tuneValue     2    data.frame list     
## obsLevels     2    -none-     character
## [1] "min lambda > lambdaOpt:"
##     (Intercept)    Gender.fctrM  Q109244.fctrNo Q109244.fctrYes 
##       0.2665753      -0.2101506      -0.4308362       1.2139586 
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)"     "Gender.fctrF"    "Gender.fctrM"    "Q109244.fctrNo" 
## [5] "Q109244.fctrYes"
## [1] "myfit_mdl: train diagnostics complete: 1.596000 secs"

##          Prediction
## Reference    R    D
##         R 1950  141
##         D 1762  595
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.721673e-01   1.772539e-01   5.574714e-01   5.867683e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   8.241814e-09  7.365212e-302

##          Prediction
## Reference   R   D
##         R 484  42
##         D 447 147
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.633929e-01   1.605510e-01   5.337655e-01   5.926864e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   1.432605e-02   1.447405e-74 
## [1] "myfit_mdl: predict complete: 5.874000 secs"
##                           id                    feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet Q109244.fctr,Gender.fctr               0
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                       0.79                 0.062       0.5971118
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5480631    0.6461604       0.3580613                    0.6
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6720662        0.5721673             0.5574714
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5867683     0.1772539       0.5896897    0.5228137
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6565657       0.3658672                    0.6       0.6643789
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5633929             0.5337655             0.5926864
##   max.Kappa.OOB
## 1      0.160551
## [1] "myfit_mdl: exit: 5.887000 secs"
if (glbMdlCheckRcv) {
    # rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
    for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
        for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
            
            # Experiment specific code to avoid caret crash
    #         lcl_tune_models_df <- rbind(data.frame()
    #                             ,data.frame(method = "glmnet", parameter = "alpha", 
    #                                         vals = "0.100 0.325 0.550 0.775 1.000")
    #                             ,data.frame(method = "glmnet", parameter = "lambda",
    #                                         vals = "9.342e-02")    
    #                                     )
            
            ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
                list(
                id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats), 
                type = glb_model_type, 
    # tune.df = lcl_tune_models_df,            
                trainControl.method = "repeatedcv",
                trainControl.number = rcv_n_folds, 
                trainControl.repeats = rcv_n_repeats,
                trainControl.classProbs = glb_is_classification,
                trainControl.summaryFunction = glbMdlMetricSummaryFn,
                train.method = "glmnet", train.metric = glbMdlMetricSummary, 
                train.maximize = glbMdlMetricMaximize)),
                                indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    # Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
    tmp_models_cols <- c("id", "max.nTuningRuns",
                        glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                        grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
    print(myplot_parcoord(obs_df = subset(glb_models_df, 
                                          grepl("Max.cor.Y.rcv.", id, fixed = TRUE), 
                                            select = -feats)[, tmp_models_cols],
                          id_var = "id"))
}
        
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
#                     paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
#                                     label.minor = "rpart")
# 
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
#     id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
#     train.method = "rpart",
#     tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
#                     indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var, 
#                     fit_df=glbObsFit, OOB_df=glbObsOOB)

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = "Max.cor.Y", 
                        type = glb_model_type, trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds, 
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        trainControl.allowParallel = glbMdlAllowParallel,                        
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = "rpart")),
                    indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var, 
                    fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] "    indepVar: Q109244.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.686000 secs"
## Loading required package: rpart
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.0225 on full training set
## [1] "myfit_mdl: train complete: 2.260000 secs"
## Loading required package: rpart.plot

## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7, 
##     cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2, 
##     surrogatestyle = 0, maxdepth = 30, xval = 0))
##   n= 4448 
## 
##           CP nsplit rel error
## 1 0.08990913      0 1.0000000
## 2 0.05930177      1 0.9100909
## 3 0.02247728      2 0.8507891
## 
## Variable importance
## Q109244.fctrYes  Q109244.fctrNo    Gender.fctrM    Gender.fctrF 
##              83              15               1               1 
## 
## Node number 1: 4448 observations,    complexity param=0.08990913
##   predicted class=D  expected loss=0.4700989  P(node) =1
##     class counts:  2091  2357
##    probabilities: 0.470 0.530 
##   left son=2 (3712 obs) right son=3 (736 obs)
##   Primary splits:
##       Q109244.fctrYes < 0.5 to the left,  improve=136.83150, (0 missing)
##       Q109244.fctrNo  < 0.5 to the right, improve= 84.31128, (0 missing)
##       Gender.fctrM    < 0.5 to the right, improve= 24.39999, (0 missing)
##       Gender.fctrF    < 0.5 to the left,  improve= 22.65952, (0 missing)
## 
## Node number 2: 3712 observations,    complexity param=0.05930177
##   predicted class=R  expected loss=0.4746767  P(node) =0.8345324
##     class counts:  1950  1762
##    probabilities: 0.525 0.475 
##   left son=4 (1980 obs) right son=5 (1732 obs)
##   Primary splits:
##       Q109244.fctrNo < 0.5 to the right, improve=24.259840, (0 missing)
##       Gender.fctrM   < 0.5 to the right, improve=10.189980, (0 missing)
##       Gender.fctrF   < 0.5 to the left,  improve= 8.193561, (0 missing)
##   Surrogate splits:
##       Gender.fctrM < 0.5 to the right, agree=0.571, adj=0.080, (0 split)
##       Gender.fctrF < 0.5 to the left,  agree=0.563, adj=0.063, (0 split)
## 
## Node number 3: 736 observations
##   predicted class=D  expected loss=0.1915761  P(node) =0.1654676
##     class counts:   141   595
##    probabilities: 0.192 0.808 
## 
## Node number 4: 1980 observations
##   predicted class=R  expected loss=0.4212121  P(node) =0.4451439
##     class counts:  1146   834
##    probabilities: 0.579 0.421 
## 
## Node number 5: 1732 observations
##   predicted class=D  expected loss=0.4642032  P(node) =0.3893885
##     class counts:   804   928
##    probabilities: 0.464 0.536 
## 
## n= 4448 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
## 1) root 4448 2091 D (0.4700989 0.5299011)  
##   2) Q109244.fctrYes< 0.5 3712 1762 R (0.5253233 0.4746767)  
##     4) Q109244.fctrNo>=0.5 1980  834 R (0.5787879 0.4212121) *
##     5) Q109244.fctrNo< 0.5 1732  804 D (0.4642032 0.5357968) *
##   3) Q109244.fctrYes>=0.5 736  141 D (0.1915761 0.8084239) *
## [1] "myfit_mdl: train diagnostics complete: 3.074000 secs"

##          Prediction
## Reference    R    D
##         R 1950  141
##         D 1762  595
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.721673e-01   1.772539e-01   5.574714e-01   5.867683e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   8.241814e-09  7.365212e-302

##          Prediction
## Reference   R   D
##         R 484  42
##         D 447 147
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.633929e-01   1.605510e-01   5.337655e-01   5.926864e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   1.432605e-02   1.447405e-74 
## [1] "myfit_mdl: predict complete: 7.695000 secs"
##                     id                    feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart Q109244.fctr,Gender.fctr               5
##   min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1                      1.566                 0.019       0.5971118
##   max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1    0.5480631    0.6461604       0.3676308                   0.55
##   max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1       0.6720662         0.600045             0.5574714
##   max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1             0.5867683     0.1947896       0.5896897    0.5228137
##   max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1    0.6565657       0.3774772                   0.55       0.6643789
##   max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1        0.5633929             0.5337655             0.5926864
##   max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1      0.160551          0.0124035      0.02559319
## [1] "myfit_mdl: exit: 7.710000 secs"
if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Max.cor.Y.Time.Poly", 
            type = glb_model_type, trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if ((length(glbFeatsDateTime) > 0) && 
    (sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
               names(glbObsAll))) > 0)) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars, 
            grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
                        names(glbObsAll), value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Time.Lag", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

if (length(glbFeatsText) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
                                    label.minor = "glmnet")

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.nonTP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,                                
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyT", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)

    indepVars <- c(max_cor_y_x_vars)
    for (txtFeat in names(glbFeatsText))
        indepVars <- union(indepVars, 
            grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
                        names(glbObsAll), perl = TRUE, value = TRUE))
    indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
    ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
        id.prefix = "Max.cor.Y.Text.onlyP", 
        type = glb_model_type, 
        tune.df = glbMdlTuneParams,        
        trainControl.method = "repeatedcv",
        trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,        
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method = "glmnet")),
        indepVar = indepVars,
        rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
}

# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA), 
                                subset(glb_feats_df, nzv)$id)) > 0) {
    fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                    paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
                                    label.minor = "glmnet")

    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
        id.prefix="Interact.High.cor.Y", 
        type=glb_model_type, trainControl.method="repeatedcv",
        trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
        trainControl.classProbs = glb_is_classification,
        trainControl.summaryFunction = glbMdlMetricSummaryFn,
        trainControl.allowParallel = glbMdlAllowParallel,
        train.metric = glbMdlMetricSummary, 
        train.maximize = glbMdlMetricMaximize,    
        train.method="glmnet")),
        indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
        rsp_var=glb_rsp_var, 
        fit_df=glbObsFit, OOB_df=glbObsOOB)
}    
##                              label step_major step_minor label_minor
## 4   fit.models_0_Max.cor.Y.rcv.*X*          1          3      glmnet
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
##       bgn     end elapsed
## 4 137.327 150.965  13.638
## 5 150.965      NA      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Interact.High.cor.Y##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr"
## [1] "myfit_mdl: setup complete: 0.704000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.00248 on full training set
## [1] "myfit_mdl: train complete: 6.005000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha

##             Length Class      Mode     
## a0            69   -none-     numeric  
## beta        3588   dgCMatrix  S4       
## df            69   -none-     numeric  
## dim            2   -none-     numeric  
## lambda        69   -none-     numeric  
## dev.ratio     69   -none-     numeric  
## nulldev        1   -none-     numeric  
## npasses        1   -none-     numeric  
## jerr           1   -none-     numeric  
## offset         1   -none-     logical  
## classnames     2   -none-     character
## call           5   -none-     call     
## nobs           1   -none-     numeric  
## lambdaOpt      1   -none-     numeric  
## xNames        52   -none-     character
## problemType    1   -none-     character
## tuneValue      2   data.frame list     
## obsLevels      2   -none-     character
## [1] "min lambda > lambdaOpt:"
##                        (Intercept)                       Gender.fctrM 
##                        0.214462392                       -0.145896015 
##                     Q109244.fctrNo                    Q109244.fctrYes 
##                       -0.216275985                        0.903504361 
##      Q109244.fctrNA:Q100689.fctrNo     Q109244.fctrYes:Q100689.fctrNo 
##                        0.208349081                       -0.004870424 
##     Q109244.fctrNA:Q100689.fctrYes     Q109244.fctrNo:Q100689.fctrYes 
##                        0.397412088                        0.048879968 
##    Q109244.fctrYes:Q100689.fctrYes      Q109244.fctrNA:Q106272.fctrNo 
##                        0.262128025                        0.066457216 
##      Q109244.fctrNo:Q106272.fctrNo     Q109244.fctrYes:Q106272.fctrNo 
##                        0.074008287                       -0.069508937 
##     Q109244.fctrNA:Q106272.fctrYes     Q109244.fctrNo:Q106272.fctrYes 
##                       -0.125437842                       -0.155157463 
##    Q109244.fctrYes:Q106272.fctrYes  Q109244.fctrNA:Q108855.fctrUmm... 
##                        0.019996659                       -0.325540110 
##  Q109244.fctrNo:Q108855.fctrUmm... Q109244.fctrYes:Q108855.fctrUmm... 
##                        0.065090912                        0.025966636 
##    Q109244.fctrNo:Q108855.fctrYes!     Q109244.fctrNA:Q120472.fctrArt 
##                       -0.166309419                        0.045175676 
##    Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience 
##                        0.050613747                       -0.081869305 
## Q109244.fctrNo:Q120472.fctrScience      Q109244.fctrNA:Q122771.fctrPc 
##                       -0.045206278                        0.026381445 
##      Q109244.fctrNo:Q122771.fctrPc      Q109244.fctrNA:Q122771.fctrPt 
##                       -0.079571797                       -0.056075353 
##      Q109244.fctrNo:Q122771.fctrPt     Q109244.fctrYes:Q122771.fctrPt 
##                       -0.312370610                       -0.184990100 
##      Q109244.fctrNA:Q123621.fctrNo     Q109244.fctrYes:Q123621.fctrNo 
##                       -0.055648138                        0.250541256 
##     Q109244.fctrNo:Q123621.fctrYes    Q109244.fctrYes:Q123621.fctrYes 
##                       -0.118706388                        0.234767924 
##       Q109244.fctrNA:Q98078.fctrNo       Q109244.fctrNo:Q98078.fctrNo 
##                        0.041213383                        0.052285528 
##      Q109244.fctrNA:Q98078.fctrYes     Q109244.fctrYes:Q98078.fctrYes 
##                        0.124665272                        0.101993831 
##       Q109244.fctrNA:Q99480.fctrNo       Q109244.fctrNo:Q99480.fctrNo 
##                        0.285510277                        0.345084748 
##      Q109244.fctrYes:Q99480.fctrNo      Q109244.fctrNA:Q99480.fctrYes 
##                        0.054445838                       -0.272288871 
## [1] "max lambda < lambdaOpt:"
##                        (Intercept)                       Gender.fctrM 
##                        0.215265192                       -0.146631129 
##                     Q109244.fctrNo                    Q109244.fctrYes 
##                       -0.232130198                        0.919519640 
##      Q109244.fctrNA:Q100689.fctrNo     Q109244.fctrYes:Q100689.fctrNo 
##                        0.218265269                       -0.019284614 
##     Q109244.fctrNA:Q100689.fctrYes     Q109244.fctrNo:Q100689.fctrYes 
##                        0.407359039                        0.052982570 
##    Q109244.fctrYes:Q100689.fctrYes      Q109244.fctrNA:Q106272.fctrNo 
##                        0.257862986                        0.066427041 
##      Q109244.fctrNo:Q106272.fctrNo     Q109244.fctrYes:Q106272.fctrNo 
##                        0.075834582                       -0.093768371 
##     Q109244.fctrNA:Q106272.fctrYes     Q109244.fctrNo:Q106272.fctrYes 
##                       -0.132132143                       -0.157097270 
##    Q109244.fctrYes:Q106272.fctrYes  Q109244.fctrNA:Q108855.fctrUmm... 
##                        0.006954145                       -0.334091989 
##  Q109244.fctrNo:Q108855.fctrUmm... Q109244.fctrYes:Q108855.fctrUmm... 
##                        0.073183683                        0.029348178 
##    Q109244.fctrNo:Q108855.fctrYes!     Q109244.fctrNA:Q120472.fctrArt 
##                       -0.161489197                        0.047013857 
##    Q109244.fctrYes:Q120472.fctrArt Q109244.fctrNA:Q120472.fctrScience 
##                        0.053722253                       -0.085734575 
## Q109244.fctrNo:Q120472.fctrScience      Q109244.fctrNA:Q122771.fctrPc 
##                       -0.046328701                        0.031858306 
##      Q109244.fctrNo:Q122771.fctrPc      Q109244.fctrNA:Q122771.fctrPt 
##                       -0.086494530                       -0.058868145 
##      Q109244.fctrNo:Q122771.fctrPt     Q109244.fctrYes:Q122771.fctrPt 
##                       -0.320607007                       -0.200384753 
##      Q109244.fctrNA:Q123621.fctrNo     Q109244.fctrYes:Q123621.fctrNo 
##                       -0.062593721                        0.268705396 
##     Q109244.fctrNo:Q123621.fctrYes    Q109244.fctrYes:Q123621.fctrYes 
##                       -0.120160110                        0.251231071 
##       Q109244.fctrNA:Q98078.fctrNo       Q109244.fctrNo:Q98078.fctrNo 
##                        0.050653191                        0.069505105 
##      Q109244.fctrNA:Q98078.fctrYes      Q109244.fctrNo:Q98078.fctrYes 
##                        0.134149587                        0.017836961 
##     Q109244.fctrYes:Q98078.fctrYes       Q109244.fctrNA:Q99480.fctrNo 
##                        0.109598128                        0.281737023 
##       Q109244.fctrNo:Q99480.fctrNo      Q109244.fctrYes:Q99480.fctrNo 
##                        0.348068744                        0.053827527 
##      Q109244.fctrNA:Q99480.fctrYes     Q109244.fctrYes:Q99480.fctrYes 
##                       -0.284681230                       -0.012138329 
## [1] "myfit_mdl: train diagnostics complete: 6.650000 secs"

##          Prediction
## Reference    R    D
##         R 1929  162
##         D 1715  642
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.780126e-01   1.870655e-01   5.633390e-01   5.925837e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   6.342747e-11  4.877355e-281

##          Prediction
## Reference   R   D
##         R 481  45
##         D 433 161
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.732143e-01   1.779779e-01   5.436402e-01   6.024028e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   2.187684e-03   4.121616e-70 
## [1] "myfit_mdl: predict complete: 12.210000 secs"
##                                id
## 1 Interact.High.cor.Y##rcv#glmnet
##                                                                                                                                                                                                                                    feats
## 1 Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                      5.276                 0.355
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6184781    0.5958871    0.6410692       0.3319465
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.65       0.6727114        0.6058167
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1              0.563339             0.5925837     0.2088694
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.6031353    0.5665399    0.6397306       0.3571392
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.65       0.6680556        0.5732143
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5436402             0.6024028     0.1779779
##   max.AccuracySD.fit max.KappaSD.fit
## 1          0.0131213      0.02732571
## [1] "myfit_mdl: exit: 12.224000 secs"
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df, 
                        paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
                                     label.minor = "glmnet")
##                              label step_major step_minor label_minor
## 5 fit.models_0_Interact.High.cor.Y          1          4      glmnet
## 6           fit.models_0_Low.cor.X          1          5      glmnet
##       bgn     end elapsed
## 5 150.965 163.219  12.254
## 6 163.220      NA      NA
indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = "Low.cor.X", 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,        
            trainControl.method = "repeatedcv",
            trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = "glmnet")),
        indepVar = indepVar, rsp_var = glb_rsp_var, 
        fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.697000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 24.124000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             88  -none-     numeric  
## beta        20416  dgCMatrix  S4       
## df             88  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         88  -none-     numeric  
## dev.ratio      88  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        232  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##              0.1920840842             -0.1005414034 
##                Edn.fctr^6                Edn.fctr^7 
##              0.0334860119              0.0710425863 
##              Gender.fctrM             Hhold.fctrMKy 
##             -0.0794064511             -0.1371778566 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##              0.4997234624              0.0176790778 
##             Hhold.fctrSKy             Income.fctr.Q 
##              0.1011680811             -0.0816169876 
##             Income.fctr.C             Income.fctr^4 
##             -0.1216519159             -0.0166494558 
##            Q100010.fctrNo           Q100680.fctrYes 
##              0.0094368536              0.0029292851 
##           Q100689.fctrYes     Q101162.fctrPessimist 
##              0.0884714668             -0.0071088785 
##           Q101163.fctrDad           Q101163.fctrMom 
##             -0.1073473633              0.0759596110 
##            Q101596.fctrNo           Q102687.fctrYes 
##             -0.0036990765              0.0309988067 
##            Q104996.fctrNo           Q104996.fctrYes 
##             -0.0444993808              0.0096111358 
##           Q105655.fctrYes            Q105840.fctrNo 
##             -0.0403102885             -0.0039386021 
##            Q106042.fctrNo           Q106272.fctrYes 
##             -0.0351407304             -0.0370732762 
##            Q106389.fctrNo            Q106997.fctrGr 
##             -0.0642024577             -0.0433734852 
##            Q106997.fctrYy           Q107491.fctrYes 
##              0.0814150590              0.0165630338 
##        Q108342.fctrOnline          Q108855.fctrYes! 
##              0.0631187924             -0.0426051502 
## Q108950.fctrRisk-friendly            Q109244.fctrNo 
##              0.0371748796             -0.3676230271 
##           Q109244.fctrYes           Q110740.fctrMac 
##              0.7526090401              0.0158364523 
##            Q110740.fctrPC           Q111220.fctrYes 
##             -0.0902880024              0.0957826931 
##           Q111848.fctrYes            Q112478.fctrNo 
##              0.0250589583             -0.0652435089 
##            Q113181.fctrNo           Q113181.fctrYes 
##              0.0925300327             -0.1002574917 
##           Q113992.fctrYes            Q115390.fctrNo 
##              0.0125173544             -0.0801709946 
##           Q115390.fctrYes            Q115611.fctrNo 
##              0.0226179572              0.1209408406 
##           Q115611.fctrYes            Q115899.fctrCs 
##             -0.3209136716              0.0774284066 
##            Q115899.fctrMe          Q116197.fctrA.M. 
##             -0.0123327062             -0.0248274337 
##         Q116881.fctrHappy         Q116881.fctrRight 
##              0.0742138891             -0.1383231722 
##            Q116953.fctrNo           Q116953.fctrYes 
##             -0.0348182328              0.0562055276 
##    Q117186.fctrHot headed            Q118232.fctrId 
##             -0.0145383547              0.1136211170 
##            Q118233.fctrNo           Q118233.fctrYes 
##             -0.0170028152              0.0113463031 
##        Q119650.fctrGiving            Q119851.fctrNo 
##             -0.0007454134             -0.1103781004 
##           Q119851.fctrYes           Q120012.fctrYes 
##              0.0120497740              0.0366728192 
##            Q120014.fctrNo           Q120014.fctrYes 
##              0.0280032695             -0.0299491592 
##   Q120194.fctrStudy first            Q120379.fctrNo 
##              0.0598036091             -0.0497973859 
##           Q120379.fctrYes       Q120472.fctrScience 
##              0.1111013195             -0.0359739528 
##           Q120650.fctrYes            Q121699.fctrNo 
##             -0.0249600547             -0.0544691763 
##           Q121699.fctrYes            Q121700.fctrNo 
##              0.0374055344             -0.0072164530 
##           Q121700.fctrYes           Q122120.fctrYes 
##              0.0163285151             -0.0198345229 
##            Q122771.fctrPt            Q123464.fctrNo 
##             -0.1085074600             -0.0188472361 
##            Q124122.fctrNo           Q124122.fctrYes 
##             -0.0315353659              0.0006062715 
##            Q124742.fctrNo             Q96024.fctrNo 
##              0.0312573363              0.0189226203 
##     Q98059.fctrOnly-child            Q98059.fctrYes 
##             -0.0023811587              0.0653704563 
##             Q98197.fctrNo            Q98197.fctrYes 
##              0.1793569818             -0.0826453929 
##             Q98578.fctrNo             Q98869.fctrNo 
##             -0.0365491345              0.2587862534 
##             Q99480.fctrNo            Q99480.fctrYes 
##              0.1316394499             -0.0381190876 
##            YOB.Age.fctr.L            YOB.Age.fctr.Q 
##              0.1159818986              0.0236491601 
##            YOB.Age.fctr^4            YOB.Age.fctr^6 
##              0.0449093485              0.0053748587 
##            YOB.Age.fctr^7            YOB.Age.fctr^8 
##             -0.0412125185             -0.0632525584 
## [1] "max lambda < lambdaOpt:"
##                (Intercept)                 Edn.fctr^4 
##                0.190242606               -0.113726050 
##                 Edn.fctr^6                 Edn.fctr^7 
##                0.039985577                0.076765149 
##               Gender.fctrM              Hhold.fctrMKy 
##               -0.079499199               -0.139799243 
##              Hhold.fctrPKn              Hhold.fctrSKn 
##                0.516895044                0.023441414 
##              Hhold.fctrSKy              Income.fctr.Q 
##                0.112262855               -0.086764305 
##              Income.fctr.C              Income.fctr^4 
##               -0.131008068               -0.022207230 
##              Income.fctr^6             Q100010.fctrNo 
##                0.005184510                0.015575737 
##            Q100680.fctrYes            Q100689.fctrYes 
##                0.005695513                0.096910592 
##      Q101162.fctrPessimist            Q101163.fctrDad 
##               -0.009462485               -0.112012131 
##            Q101163.fctrMom             Q101596.fctrNo 
##                0.077204753               -0.011035252 
##            Q102687.fctrYes             Q103293.fctrNo 
##                0.036506262               -0.003992889 
##             Q104996.fctrNo            Q104996.fctrYes 
##               -0.046964218                0.013628231 
##            Q105655.fctrYes             Q105840.fctrNo 
##               -0.045689342               -0.004398376 
##             Q106042.fctrNo            Q106272.fctrYes 
##               -0.037204139               -0.042078733 
##             Q106389.fctrNo             Q106997.fctrGr 
##               -0.069884472               -0.045946207 
##             Q106997.fctrYy            Q107491.fctrYes 
##                0.087529010                0.022505522 
##         Q108342.fctrOnline           Q108855.fctrYes! 
##                0.068972019               -0.048326214 
##  Q108950.fctrRisk-friendly             Q109244.fctrNo 
##                0.042397185               -0.374416559 
##            Q109244.fctrYes            Q110740.fctrMac 
##                0.763571824                0.016457805 
##             Q110740.fctrPC            Q111220.fctrYes 
##               -0.095759349                0.102165768 
##            Q111848.fctrYes            Q112270.fctrYes 
##                0.029246498                0.002532131 
##             Q112478.fctrNo             Q113181.fctrNo 
##               -0.071674308                0.094659726 
##            Q113181.fctrYes            Q113992.fctrYes 
##               -0.101925125                0.018758347 
##            Q114152.fctrYes     Q114386.fctrMysterious 
##                0.001215794                0.005550234 
##             Q115390.fctrNo            Q115390.fctrYes 
##               -0.085128214                0.024943706 
##             Q115602.fctrNo             Q115611.fctrNo 
##               -0.006129422                0.121429244 
##            Q115611.fctrYes             Q115899.fctrCs 
##               -0.328446834                0.082573783 
##             Q115899.fctrMe           Q116197.fctrA.M. 
##               -0.012353330               -0.032450966 
##          Q116881.fctrHappy          Q116881.fctrRight 
##                0.078603456               -0.141878310 
##             Q116953.fctrNo            Q116953.fctrYes 
##               -0.036656763                0.063262470 
##     Q117186.fctrHot headed Q117193.fctrStandard hours 
##               -0.019710980               -0.002981009 
##             Q118232.fctrId             Q118233.fctrNo 
##                0.120887721               -0.021333073 
##            Q118233.fctrYes         Q119650.fctrGiving 
##                0.014400900               -0.006146699 
##             Q119851.fctrNo            Q119851.fctrYes 
##               -0.113773685                0.013486484 
##            Q120012.fctrYes             Q120014.fctrNo 
##                0.040890779                0.032195937 
##            Q120014.fctrYes    Q120194.fctrStudy first 
##               -0.032533254                0.065256829 
##             Q120379.fctrNo            Q120379.fctrYes 
##               -0.050149716                0.118837105 
##        Q120472.fctrScience            Q120650.fctrYes 
##               -0.037775351               -0.030467057 
##             Q121699.fctrNo            Q121699.fctrYes 
##               -0.051633489                0.044689201 
##             Q121700.fctrNo            Q121700.fctrYes 
##               -0.011071443                0.016970195 
##            Q122120.fctrYes             Q122771.fctrPt 
##               -0.024872502               -0.115816557 
##             Q123464.fctrNo             Q124122.fctrNo 
##               -0.024443028               -0.034722635 
##            Q124122.fctrYes             Q124742.fctrNo 
##                0.004874931                0.039013147 
##              Q96024.fctrNo      Q98059.fctrOnly-child 
##                0.023646239               -0.009596390 
##             Q98059.fctrYes              Q98197.fctrNo 
##                0.073367383                0.184718092 
##             Q98197.fctrYes              Q98578.fctrNo 
##               -0.083886484               -0.043820323 
##              Q98869.fctrNo              Q99480.fctrNo 
##                0.266626746                0.134742947 
##             Q99480.fctrYes             YOB.Age.fctr.L 
##               -0.043472697                0.130623922 
##             YOB.Age.fctr.Q             YOB.Age.fctr^4 
##                0.036878348                0.054188683 
##             YOB.Age.fctr^6             YOB.Age.fctr^7 
##                0.013010153               -0.049328694 
##             YOB.Age.fctr^8 
##               -0.071002509 
## [1] "myfit_mdl: train diagnostics complete: 24.794000 secs"

##          Prediction
## Reference    R    D
##         R 1844  247
##         D 1431  926
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.227518e-01   2.662403e-01   6.083184e-01   6.370239e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   5.105863e-36  2.163939e-183

##          Prediction
## Reference   R   D
##         R 476  50
##         D 419 175
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.812500e-01   1.918521e-01   5.517282e-01   6.103439e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   3.471802e-04   9.306917e-65 
## [1] "myfit_mdl: predict complete: 34.923000 secs"
##                      id
## 1 Low.cor.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     23.305                 2.128
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6534188    0.5872788    0.7195588       0.2783723
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6872903        0.6254518
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6083184             0.6370239     0.2446067
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.6261026     0.526616    0.7255892        0.315743
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.65       0.6699507          0.58125
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5517282             0.6103439     0.1918521
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01290483      0.02676153
## [1] "myfit_mdl: exit: 34.938000 secs"
fit.models_0_chunk_df <- 
    myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
                label.minor = "teardown")
##                    label step_major step_minor label_minor    bgn     end
## 6 fit.models_0_Low.cor.X          1          5      glmnet 163.22 198.211
## 7       fit.models_0_end          1          6    teardown 198.22      NA
##   elapsed
## 6      35
## 7      NA
rm(ret_lst)

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##        label step_major step_minor label_minor     bgn     end elapsed
## 4 fit.models          4          0           0 124.554 198.235  73.681
## 5 fit.models          4          1           1 198.236      NA      NA
fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_1_bgn          1          0       setup 202.575  NA      NA
# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???

#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
    fit.models_1_chunk_df <- 
        myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
                    major.inc = FALSE, label.minor = "setup")

    indepVar <- NULL;

    if (grepl("\\.Interact", mdl_id_pfx)) {
        if (is.null(topindep_var) && is.null(interact_vars)) {
        #   select best glmnet model upto now
            dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
                                     glb_models_df)
            dsp_models_df <- subset(dsp_models_df, 
                                    grepl(".glmnet", id, fixed = TRUE))
            bst_mdl_id <- dsp_models_df$id[1]
            mdl_id_pfx <- 
                paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
                      collapse=".")
        #   select important features
            if (is.null(bst_featsimp_df <- 
                        myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
                warning("Base model for RFE.Interact: ", bst_mdl_id, 
                        " has no important features")
                next
            }    
            
            topindep_ix <- 1
            while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
                topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
                if (grepl(".fctr", topindep_var, fixed=TRUE))
                    topindep_var <- 
                        paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
                if (topindep_var %in% names(glbFeatsInteractionOnly)) {
                    topindep_var <- NULL; topindep_ix <- topindep_ix + 1
                } else break
            }
            
        #   select features with importance > max(10, importance of .rnorm) & is not highest
        #       combine factor dummy features to just the factor feature
            if (length(pos_rnorm <- 
                       grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
                imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
                imp_rnorm <- NA    
            imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
            interact_vars <- 
                tail(row.names(subset(bst_featsimp_df, 
                                      imp > imp_cutoff)), -1)
            if (length(interact_vars) > 0) {
                interact_vars <-
                    myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
                interact_vars <- 
                    interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
            }
            ### bid0_sp only
#             interact_vars <- c(
#     "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
#     "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
#     "D.chrs.n.log", "color.fctr"
#     # , "condition.fctr", "prdl.my.descr.fctr"
#                                 )
#            interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
            ###
            indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
            indepVar <- setdiff(indepVar, topindep_var)
            if (length(interact_vars) > 0) {
                indepVar <- 
                    setdiff(indepVar, myextract_actual_feats(interact_vars))
                indepVar <- c(indepVar, 
                    paste(topindep_var, setdiff(interact_vars, topindep_var), 
                          sep = "*"))
            } else indepVar <- union(indepVar, topindep_var)
        }
    }
    
    if (is.null(indepVar))
        indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]

    if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
        indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
    
    if (is.null(indepVar))
        indepVar <- mygetIndepVar(glb_feats_df)
    
    if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {    
        indepVar <- 
            eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
    }    

    indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
    
    if (grepl("\\.Interact", mdl_id_pfx)) { 
        # if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
        if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
            if (!is.null(glbMdlFamilies[["Best.Interact"]]))
                glbMdlFamilies[[mdl_id_pfx]] <-
                    glbMdlFamilies[["Best.Interact"]]
        }
    }
    
    if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
        fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                         glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    } else fitobs_df <- glbObsFit

    if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
        mdl_methods <- glbMdlMethods else
        mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]    

    for (method in mdl_methods) {
        if (method %in% c("rpart", "rf")) {
            # rpart:    fubar's the tree
            # rf:       skip the scenario w/ .rnorm for speed
            indepVar <- setdiff(indepVar, c(".rnorm"))
            #mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
        } 

        fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, 
                            paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
                                    label.minor = method)

        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
            id.prefix = mdl_id_pfx, 
            type = glb_model_type, 
            tune.df = glbMdlTuneParams,
            trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
            trainControl.number = glb_rcv_n_folds,
            trainControl.repeats = glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            trainControl.allowParallel = glbMdlAllowParallel,            
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method = method)),
            indepVar = indepVar, rsp_var = glb_rsp_var, 
            fit_df = fitobs_df, OOB_df = glbObsOOB)
        
#         ntv_mdl <- glmnet(x = as.matrix(
#                               fitobs_df[, indepVar]), 
#                           y = as.factor(as.character(
#                               fitobs_df[, glb_rsp_var])),
#                           family = "multinomial")
#         bgn = 1; end = 100;
#         ntv_mdl <- glmnet(x = as.matrix(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]), 
#                           y = as.factor(as.character(
#                               subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
#                           family = "multinomial")
    }
}
##                label step_major step_minor label_minor     bgn     end
## 1   fit.models_1_bgn          1          0       setup 202.575 202.586
## 2 fit.models_1_All.X          1          1       setup 202.586      NA
##   elapsed
## 1   0.011
## 2      NA
##                label step_major step_minor label_minor     bgn     end
## 2 fit.models_1_All.X          1          1       setup 202.586 202.593
## 3 fit.models_1_All.X          1          2      glmnet 202.593      NA
##   elapsed
## 2   0.007
## 3      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.731000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.0534 on full training set
## [1] "myfit_mdl: train complete: 23.748000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda

##             Length Class      Mode     
## a0             88  -none-     numeric  
## beta        20416  dgCMatrix  S4       
## df             88  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         88  -none-     numeric  
## dev.ratio      88  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        232  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##              0.1920840842             -0.1005414034 
##                Edn.fctr^6                Edn.fctr^7 
##              0.0334860119              0.0710425863 
##              Gender.fctrM             Hhold.fctrMKy 
##             -0.0794064511             -0.1371778566 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##              0.4997234624              0.0176790778 
##             Hhold.fctrSKy             Income.fctr.Q 
##              0.1011680811             -0.0816169876 
##             Income.fctr.C             Income.fctr^4 
##             -0.1216519159             -0.0166494558 
##            Q100010.fctrNo           Q100680.fctrYes 
##              0.0094368536              0.0029292851 
##           Q100689.fctrYes     Q101162.fctrPessimist 
##              0.0884714668             -0.0071088785 
##           Q101163.fctrDad           Q101163.fctrMom 
##             -0.1073473633              0.0759596110 
##            Q101596.fctrNo           Q102687.fctrYes 
##             -0.0036990765              0.0309988067 
##            Q104996.fctrNo           Q104996.fctrYes 
##             -0.0444993808              0.0096111358 
##           Q105655.fctrYes            Q105840.fctrNo 
##             -0.0403102885             -0.0039386021 
##            Q106042.fctrNo           Q106272.fctrYes 
##             -0.0351407304             -0.0370732762 
##            Q106389.fctrNo            Q106997.fctrGr 
##             -0.0642024577             -0.0433734852 
##            Q106997.fctrYy           Q107491.fctrYes 
##              0.0814150590              0.0165630338 
##        Q108342.fctrOnline          Q108855.fctrYes! 
##              0.0631187924             -0.0426051502 
## Q108950.fctrRisk-friendly            Q109244.fctrNo 
##              0.0371748796             -0.3676230271 
##           Q109244.fctrYes           Q110740.fctrMac 
##              0.7526090401              0.0158364523 
##            Q110740.fctrPC           Q111220.fctrYes 
##             -0.0902880024              0.0957826931 
##           Q111848.fctrYes            Q112478.fctrNo 
##              0.0250589583             -0.0652435089 
##            Q113181.fctrNo           Q113181.fctrYes 
##              0.0925300327             -0.1002574917 
##           Q113992.fctrYes            Q115390.fctrNo 
##              0.0125173544             -0.0801709946 
##           Q115390.fctrYes            Q115611.fctrNo 
##              0.0226179572              0.1209408406 
##           Q115611.fctrYes            Q115899.fctrCs 
##             -0.3209136716              0.0774284066 
##            Q115899.fctrMe          Q116197.fctrA.M. 
##             -0.0123327062             -0.0248274337 
##         Q116881.fctrHappy         Q116881.fctrRight 
##              0.0742138891             -0.1383231722 
##            Q116953.fctrNo           Q116953.fctrYes 
##             -0.0348182328              0.0562055276 
##    Q117186.fctrHot headed            Q118232.fctrId 
##             -0.0145383547              0.1136211170 
##            Q118233.fctrNo           Q118233.fctrYes 
##             -0.0170028152              0.0113463031 
##        Q119650.fctrGiving            Q119851.fctrNo 
##             -0.0007454134             -0.1103781004 
##           Q119851.fctrYes           Q120012.fctrYes 
##              0.0120497740              0.0366728192 
##            Q120014.fctrNo           Q120014.fctrYes 
##              0.0280032695             -0.0299491592 
##   Q120194.fctrStudy first            Q120379.fctrNo 
##              0.0598036091             -0.0497973859 
##           Q120379.fctrYes       Q120472.fctrScience 
##              0.1111013195             -0.0359739528 
##           Q120650.fctrYes            Q121699.fctrNo 
##             -0.0249600547             -0.0544691763 
##           Q121699.fctrYes            Q121700.fctrNo 
##              0.0374055344             -0.0072164530 
##           Q121700.fctrYes           Q122120.fctrYes 
##              0.0163285151             -0.0198345229 
##            Q122771.fctrPt            Q123464.fctrNo 
##             -0.1085074600             -0.0188472361 
##            Q124122.fctrNo           Q124122.fctrYes 
##             -0.0315353659              0.0006062715 
##            Q124742.fctrNo             Q96024.fctrNo 
##              0.0312573363              0.0189226203 
##     Q98059.fctrOnly-child            Q98059.fctrYes 
##             -0.0023811587              0.0653704563 
##             Q98197.fctrNo            Q98197.fctrYes 
##              0.1793569818             -0.0826453929 
##             Q98578.fctrNo             Q98869.fctrNo 
##             -0.0365491345              0.2587862534 
##             Q99480.fctrNo            Q99480.fctrYes 
##              0.1316394499             -0.0381190876 
##            YOB.Age.fctr.L            YOB.Age.fctr.Q 
##              0.1159818986              0.0236491601 
##            YOB.Age.fctr^4            YOB.Age.fctr^6 
##              0.0449093485              0.0053748587 
##            YOB.Age.fctr^7            YOB.Age.fctr^8 
##             -0.0412125185             -0.0632525584 
## [1] "max lambda < lambdaOpt:"
##                (Intercept)                 Edn.fctr^4 
##                0.190242606               -0.113726050 
##                 Edn.fctr^6                 Edn.fctr^7 
##                0.039985577                0.076765149 
##               Gender.fctrM              Hhold.fctrMKy 
##               -0.079499199               -0.139799243 
##              Hhold.fctrPKn              Hhold.fctrSKn 
##                0.516895044                0.023441414 
##              Hhold.fctrSKy              Income.fctr.Q 
##                0.112262855               -0.086764305 
##              Income.fctr.C              Income.fctr^4 
##               -0.131008068               -0.022207230 
##              Income.fctr^6             Q100010.fctrNo 
##                0.005184510                0.015575737 
##            Q100680.fctrYes            Q100689.fctrYes 
##                0.005695513                0.096910592 
##      Q101162.fctrPessimist            Q101163.fctrDad 
##               -0.009462485               -0.112012131 
##            Q101163.fctrMom             Q101596.fctrNo 
##                0.077204753               -0.011035252 
##            Q102687.fctrYes             Q103293.fctrNo 
##                0.036506262               -0.003992889 
##             Q104996.fctrNo            Q104996.fctrYes 
##               -0.046964218                0.013628231 
##            Q105655.fctrYes             Q105840.fctrNo 
##               -0.045689342               -0.004398376 
##             Q106042.fctrNo            Q106272.fctrYes 
##               -0.037204139               -0.042078733 
##             Q106389.fctrNo             Q106997.fctrGr 
##               -0.069884472               -0.045946207 
##             Q106997.fctrYy            Q107491.fctrYes 
##                0.087529010                0.022505522 
##         Q108342.fctrOnline           Q108855.fctrYes! 
##                0.068972019               -0.048326214 
##  Q108950.fctrRisk-friendly             Q109244.fctrNo 
##                0.042397185               -0.374416559 
##            Q109244.fctrYes            Q110740.fctrMac 
##                0.763571824                0.016457805 
##             Q110740.fctrPC            Q111220.fctrYes 
##               -0.095759349                0.102165768 
##            Q111848.fctrYes            Q112270.fctrYes 
##                0.029246498                0.002532131 
##             Q112478.fctrNo             Q113181.fctrNo 
##               -0.071674308                0.094659726 
##            Q113181.fctrYes            Q113992.fctrYes 
##               -0.101925125                0.018758347 
##            Q114152.fctrYes     Q114386.fctrMysterious 
##                0.001215794                0.005550234 
##             Q115390.fctrNo            Q115390.fctrYes 
##               -0.085128214                0.024943706 
##             Q115602.fctrNo             Q115611.fctrNo 
##               -0.006129422                0.121429244 
##            Q115611.fctrYes             Q115899.fctrCs 
##               -0.328446834                0.082573783 
##             Q115899.fctrMe           Q116197.fctrA.M. 
##               -0.012353330               -0.032450966 
##          Q116881.fctrHappy          Q116881.fctrRight 
##                0.078603456               -0.141878310 
##             Q116953.fctrNo            Q116953.fctrYes 
##               -0.036656763                0.063262470 
##     Q117186.fctrHot headed Q117193.fctrStandard hours 
##               -0.019710980               -0.002981009 
##             Q118232.fctrId             Q118233.fctrNo 
##                0.120887721               -0.021333073 
##            Q118233.fctrYes         Q119650.fctrGiving 
##                0.014400900               -0.006146699 
##             Q119851.fctrNo            Q119851.fctrYes 
##               -0.113773685                0.013486484 
##            Q120012.fctrYes             Q120014.fctrNo 
##                0.040890779                0.032195937 
##            Q120014.fctrYes    Q120194.fctrStudy first 
##               -0.032533254                0.065256829 
##             Q120379.fctrNo            Q120379.fctrYes 
##               -0.050149716                0.118837105 
##        Q120472.fctrScience            Q120650.fctrYes 
##               -0.037775351               -0.030467057 
##             Q121699.fctrNo            Q121699.fctrYes 
##               -0.051633489                0.044689201 
##             Q121700.fctrNo            Q121700.fctrYes 
##               -0.011071443                0.016970195 
##            Q122120.fctrYes             Q122771.fctrPt 
##               -0.024872502               -0.115816557 
##             Q123464.fctrNo             Q124122.fctrNo 
##               -0.024443028               -0.034722635 
##            Q124122.fctrYes             Q124742.fctrNo 
##                0.004874931                0.039013147 
##              Q96024.fctrNo      Q98059.fctrOnly-child 
##                0.023646239               -0.009596390 
##             Q98059.fctrYes              Q98197.fctrNo 
##                0.073367383                0.184718092 
##             Q98197.fctrYes              Q98578.fctrNo 
##               -0.083886484               -0.043820323 
##              Q98869.fctrNo              Q99480.fctrNo 
##                0.266626746                0.134742947 
##             Q99480.fctrYes             YOB.Age.fctr.L 
##               -0.043472697                0.130623922 
##             YOB.Age.fctr.Q             YOB.Age.fctr^4 
##                0.036878348                0.054188683 
##             YOB.Age.fctr^6             YOB.Age.fctr^7 
##                0.013010153               -0.049328694 
##             YOB.Age.fctr^8 
##               -0.071002509 
## [1] "myfit_mdl: train diagnostics complete: 24.499000 secs"

##          Prediction
## Reference    R    D
##         R 1844  247
##         D 1431  926
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.227518e-01   2.662403e-01   6.083184e-01   6.370239e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   5.105863e-36  2.163939e-183

##          Prediction
## Reference   R   D
##         R 476  50
##         D 419 175
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.812500e-01   1.918521e-01   5.517282e-01   6.103439e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   3.471802e-04   9.306917e-65 
## [1] "myfit_mdl: predict complete: 35.118000 secs"
##                  id
## 1 All.X##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     22.908                 2.054
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6534188    0.5872788    0.7195588       0.2783723
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6872903        0.6254518
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6083184             0.6370239     0.2446067
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.6261026     0.526616    0.7255892        0.315743
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.65       0.6699507          0.58125
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5517282             0.6103439     0.1918521
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01290483      0.02676153
## [1] "myfit_mdl: exit: 35.133000 secs"
##                label step_major step_minor label_minor     bgn     end
## 3 fit.models_1_All.X          1          2      glmnet 202.593 237.732
## 4 fit.models_1_All.X          1          3         glm 237.733      NA
##   elapsed
## 3  35.139
## 4      NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.732000 secs"
## + Fold1.Rep1: parameter=none 
## - Fold1.Rep1: parameter=none 
## + Fold2.Rep1: parameter=none 
## - Fold2.Rep1: parameter=none 
## + Fold3.Rep1: parameter=none 
## - Fold3.Rep1: parameter=none 
## + Fold1.Rep2: parameter=none 
## - Fold1.Rep2: parameter=none 
## + Fold2.Rep2: parameter=none 
## - Fold2.Rep2: parameter=none 
## + Fold3.Rep2: parameter=none 
## - Fold3.Rep2: parameter=none 
## + Fold1.Rep3: parameter=none 
## - Fold1.Rep3: parameter=none 
## + Fold2.Rep3: parameter=none 
## - Fold2.Rep3: parameter=none 
## + Fold3.Rep3: parameter=none 
## - Fold3.Rep3: parameter=none 
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 13.259000 secs"

## 
## Call:
## NULL
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5365  -1.0394   0.4278   1.0349   2.3472  
## 
## Coefficients:
##                                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                   0.4037443  0.2594431   1.556 0.119662    
## .rnorm                       -0.0144735  0.0334725  -0.432 0.665451    
## Edn.fctr.L                   -0.0853170  0.1551935  -0.550 0.582493    
## Edn.fctr.Q                   -0.0145452  0.1458234  -0.100 0.920547    
## Edn.fctr.C                   -0.0128867  0.1274767  -0.101 0.919479    
## `Edn.fctr^4`                 -0.3442017  0.1257839  -2.736 0.006211 ** 
## `Edn.fctr^5`                 -0.0624099  0.1161193  -0.537 0.590947    
## `Edn.fctr^6`                  0.1421535  0.1052526   1.351 0.176826    
## `Edn.fctr^7`                  0.1957287  0.1162951   1.683 0.092368 .  
## Gender.fctrF                 -0.3849892  0.2401749  -1.603 0.108945    
## Gender.fctrM                 -0.4595950  0.2360077  -1.947 0.051490 .  
## Hhold.fctrMKn                 0.0340870  0.1822539   0.187 0.851637    
## Hhold.fctrMKy                -0.1340050  0.1684782  -0.795 0.426390    
## Hhold.fctrPKn                 0.9206637  0.2550394   3.610 0.000306 ***
## Hhold.fctrPKy                 0.2295176  0.3396652   0.676 0.499220    
## Hhold.fctrSKn                 0.1955830  0.1425674   1.372 0.170106    
## Hhold.fctrSKy                 0.3657954  0.2459453   1.487 0.136935    
## Income.fctr.L                -0.1002526  0.1078238  -0.930 0.352484    
## Income.fctr.Q                -0.1751734  0.0989709  -1.770 0.076736 .  
## Income.fctr.C                -0.2569733  0.0962966  -2.669 0.007618 ** 
## `Income.fctr^4`              -0.1112022  0.0936783  -1.187 0.235202    
## `Income.fctr^5`              -0.0004333  0.0952860  -0.005 0.996372    
## `Income.fctr^6`               0.0910037  0.0926831   0.982 0.326159    
## Q100010.fctrNo                0.2532892  0.2169400   1.168 0.242987    
## Q100010.fctrYes               0.1846774  0.1986899   0.929 0.352643    
## Q100562.fctrNo                0.0531004  0.2003390   0.265 0.790969    
## Q100562.fctrYes               0.0619535  0.1763393   0.351 0.725340    
## Q100680.fctrNo               -0.2041623  0.1928685  -1.059 0.289802    
## Q100680.fctrYes              -0.1479846  0.1866402  -0.793 0.427844    
## Q100689.fctrNo                0.3907495  0.1944442   2.010 0.044477 *  
## Q100689.fctrYes               0.5517627  0.1930412   2.858 0.004260 ** 
## Q101162.fctrOptimist          0.0617722  0.1794274   0.344 0.730640    
## Q101162.fctrPessimist         0.0407708  0.1853843   0.220 0.825929    
## Q101163.fctrDad              -0.2284776  0.1588094  -1.439 0.150238    
## Q101163.fctrMom               0.0490395  0.1633177   0.300 0.763971    
## Q101596.fctrNo               -0.4893671  0.1629618  -3.003 0.002674 ** 
## Q101596.fctrYes              -0.4440094  0.1722253  -2.578 0.009935 ** 
## Q102089.fctrOwn               0.1281066  0.1639400   0.781 0.434553    
## Q102089.fctrRent              0.0916325  0.1733814   0.529 0.597151    
## Q102289.fctrNo                0.1025436  0.1681116   0.610 0.541879    
## Q102289.fctrYes               0.0543807  0.1789322   0.304 0.761190    
## Q102674.fctrNo               -0.4093310  0.2164253  -1.891 0.058581 .  
## Q102674.fctrYes              -0.3690017  0.2276504  -1.621 0.105036    
## Q102687.fctrNo                0.4021434  0.2308784   1.742 0.081544 .  
## Q102687.fctrYes               0.4752363  0.2290956   2.074 0.038042 *  
## Q102906.fctrNo                0.0150782  0.1701992   0.089 0.929407    
## Q102906.fctrYes              -0.0164693  0.1747187  -0.094 0.924901    
## Q103293.fctrNo               -0.1000088  0.1526555  -0.655 0.512385    
## Q103293.fctrYes               0.0027265  0.1545036   0.018 0.985921    
## Q104996.fctrNo               -0.0233455  0.1430365  -0.163 0.870350    
## Q104996.fctrYes               0.1359172  0.1412363   0.962 0.335879    
## Q105655.fctrNo               -0.0865104  0.1739045  -0.497 0.618865    
## Q105655.fctrYes              -0.1804958  0.1719312  -1.050 0.293803    
## Q105840.fctrNo                0.0802580  0.1755224   0.457 0.647490    
## Q105840.fctrYes               0.0689533  0.1766436   0.390 0.696276    
## Q106042.fctrNo               -0.2406816  0.1740473  -1.383 0.166710    
## Q106042.fctrYes              -0.1831419  0.1743671  -1.050 0.293569    
## Q106272.fctrNo                0.1568939  0.1948587   0.805 0.420723    
## Q106272.fctrYes               0.0210004  0.1816784   0.116 0.907977    
## Q106388.fctrNo               -0.0278438  0.2133411  -0.131 0.896161    
## Q106388.fctrYes              -0.0084882  0.2254821  -0.038 0.969971    
## Q106389.fctrNo               -0.2556684  0.2119435  -1.206 0.227700    
## Q106389.fctrYes              -0.0851427  0.2135943  -0.399 0.690174    
## Q106993.fctrNo               -0.2186972  0.2150595  -1.017 0.309194    
## Q106993.fctrYes              -0.1047666  0.1913100  -0.548 0.583948    
## Q106997.fctrGr                0.0158503  0.1928851   0.082 0.934508    
## Q106997.fctrYy                0.2733245  0.1966022   1.390 0.164456    
## Q107491.fctrNo                0.0908670  0.1815540   0.500 0.616726    
## Q107491.fctrYes               0.1579099  0.1387772   1.138 0.255176    
## Q107869.fctrNo                0.0062751  0.1460306   0.043 0.965725    
## Q107869.fctrYes              -0.0632948  0.1465056  -0.432 0.665719    
## `Q108342.fctrIn-person`       0.2456134  0.1751802   1.402 0.160897    
## Q108342.fctrOnline            0.3905191  0.1850827   2.110 0.034861 *  
## Q108343.fctrNo               -0.0932666  0.1815813  -0.514 0.607507    
## Q108343.fctrYes              -0.1535428  0.1918086  -0.800 0.423421    
## Q108617.fctrNo                0.0681151  0.1659526   0.410 0.681477    
## Q108617.fctrYes              -0.1118839  0.2071519  -0.540 0.589124    
## Q108754.fctrNo                0.0723608  0.1871754   0.387 0.699057    
## Q108754.fctrYes               0.0610898  0.1957694   0.312 0.755003    
## Q108855.fctrUmm...           -0.0670437  0.2108132  -0.318 0.750466    
## `Q108855.fctrYes!`           -0.1883058  0.2071856  -0.909 0.363416    
## Q108856.fctrSocialize        -0.1926630  0.2135423  -0.902 0.366938    
## Q108856.fctrSpace            -0.1986380  0.1991624  -0.997 0.318586    
## Q108950.fctrCautious          0.1185763  0.1558996   0.761 0.446900    
## `Q108950.fctrRisk-friendly`   0.2304749  0.1672452   1.378 0.168183    
## Q109244.fctrNo               -0.5966868  0.1484292  -4.020 5.82e-05 ***
## Q109244.fctrYes               0.8423650  0.1706646   4.936 7.98e-07 ***
## Q109367.fctrNo                0.1198020  0.1535465   0.780 0.435254    
## Q109367.fctrYes               0.0684263  0.1468452   0.466 0.641233    
## Q110740.fctrMac              -0.0158847  0.1298637  -0.122 0.902647    
## Q110740.fctrPC               -0.2236029  0.1267792  -1.764 0.077779 .  
## Q111220.fctrNo               -0.0142208  0.1396416  -0.102 0.918885    
## Q111220.fctrYes               0.1857203  0.1531284   1.213 0.225191    
## Q111580.fctrDemanding        -0.0173339  0.1532867  -0.113 0.909966    
## Q111580.fctrSupportive        0.0111346  0.1437728   0.077 0.938269    
## Q111848.fctrNo                0.0949728  0.1517066   0.626 0.531296    
## Q111848.fctrYes               0.1286246  0.1466950   0.877 0.380586    
## Q112270.fctrNo                0.1390319  0.1419959   0.979 0.327518    
## Q112270.fctrYes               0.1888612  0.1420612   1.329 0.183704    
## Q112478.fctrNo               -0.3660755  0.1736086  -2.109 0.034977 *  
## Q112478.fctrYes              -0.1532040  0.1676247  -0.914 0.360732    
## Q112512.fctrNo                0.0859518  0.1838620   0.467 0.640157    
## Q112512.fctrYes               0.0299766  0.1570307   0.191 0.848606    
## Q113181.fctrNo                0.0799640  0.1377864   0.580 0.561680    
## Q113181.fctrYes              -0.1990248  0.1433051  -1.389 0.164888    
## Q113583.fctrTalk              0.0900451  0.1977001   0.455 0.648776    
## Q113583.fctrTunes             0.1246068  0.1898217   0.656 0.511540    
## Q113584.fctrPeople           -0.1411523  0.1942598  -0.727 0.467461    
## Q113584.fctrTechnology       -0.1176025  0.1930409  -0.609 0.542385    
## Q113992.fctrNo                0.1943289  0.1550721   1.253 0.210151    
## Q113992.fctrYes               0.2819251  0.1663777   1.694 0.090172 .  
## Q114152.fctrNo               -0.1271457  0.1520273  -0.836 0.402967    
## Q114152.fctrYes              -0.0101405  0.1633915  -0.062 0.950513    
## Q114386.fctrMysterious        0.0662478  0.1534622   0.432 0.665968    
## Q114386.fctrTMI              -0.0128265  0.1569364  -0.082 0.934861    
## Q114517.fctrNo                0.1943655  0.1666779   1.166 0.243568    
## Q114517.fctrYes               0.2112956  0.1770188   1.194 0.232621    
## Q114748.fctrNo               -0.3312277  0.1768477  -1.873 0.061075 .  
## Q114748.fctrYes              -0.2977441  0.1750108  -1.701 0.088889 .  
## Q114961.fctrNo                0.2182312  0.1694179   1.288 0.197703    
## Q114961.fctrYes               0.1633945  0.1682321   0.971 0.331427    
## Q115195.fctrNo                0.0691841  0.1662648   0.416 0.677331    
## Q115195.fctrYes               0.1016507  0.1563161   0.650 0.515505    
## Q115390.fctrNo               -0.2192733  0.1499292  -1.463 0.143601    
## Q115390.fctrYes              -0.0018497  0.1404019  -0.013 0.989489    
## Q115602.fctrNo                0.0695244  0.1931883   0.360 0.718938    
## Q115602.fctrYes               0.1713497  0.1727895   0.992 0.321360    
## Q115610.fctrNo               -0.0570428  0.2041578  -0.279 0.779934    
## Q115610.fctrYes              -0.0522951  0.1805015  -0.290 0.772030    
## Q115611.fctrNo               -0.0194842  0.1903864  -0.102 0.918487    
## Q115611.fctrYes              -0.5824403  0.1955798  -2.978 0.002901 ** 
## Q115777.fctrEnd               0.0061494  0.1601912   0.038 0.969379    
## Q115777.fctrStart             0.0562863  0.1562200   0.360 0.718622    
## Q115899.fctrCs                0.2025209  0.1577967   1.283 0.199342    
## Q115899.fctrMe                0.0173814  0.1556272   0.112 0.911072    
## Q116197.fctrA.M.             -0.3797977  0.1563614  -2.429 0.015142 *  
## Q116197.fctrP.M.             -0.2689386  0.1457797  -1.845 0.065062 .  
## Q116441.fctrNo               -0.1688255  0.1765961  -0.956 0.339073    
## Q116441.fctrYes              -0.0897363  0.1897777  -0.473 0.636320    
## Q116448.fctrNo                0.1767473  0.1672473   1.057 0.290602    
## Q116448.fctrYes               0.1345875  0.1687216   0.798 0.425051    
## Q116601.fctrNo                0.2153571  0.1959974   1.099 0.271866    
## Q116601.fctrYes               0.1869677  0.1675158   1.116 0.264371    
## Q116797.fctrNo               -0.1519075  0.1693141  -0.897 0.369616    
## Q116797.fctrYes              -0.1962125  0.1744885  -1.125 0.260801    
## Q116881.fctrHappy             0.1403840  0.1646960   0.852 0.394002    
## Q116881.fctrRight            -0.1907077  0.1796898  -1.061 0.288546    
## Q116953.fctrNo                0.0133781  0.1770131   0.076 0.939756    
## Q116953.fctrYes               0.2574395  0.1666295   1.545 0.122351    
## `Q117186.fctrCool headed`     0.0042746  0.1646871   0.026 0.979293    
## `Q117186.fctrHot headed`     -0.0816835  0.1728795  -0.472 0.636578    
## `Q117193.fctrOdd hours`       0.0020904  0.1616737   0.013 0.989684    
## `Q117193.fctrStandard hours` -0.0754438  0.1540240  -0.490 0.624263    
## Q118117.fctrNo               -0.0287136  0.1490744  -0.193 0.847262    
## Q118117.fctrYes               0.0018835  0.1511387   0.012 0.990057    
## Q118232.fctrId                0.4236049  0.1471877   2.878 0.004002 ** 
## Q118232.fctrPr                0.2290509  0.1454537   1.575 0.115318    
## Q118233.fctrNo               -0.1457718  0.1865583  -0.781 0.434583    
## Q118233.fctrYes               0.0221315  0.2023382   0.109 0.912902    
## Q118237.fctrNo               -0.1548534  0.1894378  -0.817 0.413679    
## Q118237.fctrYes              -0.1295658  0.1862911  -0.696 0.486741    
## Q118892.fctrNo                0.0909613  0.1321809   0.688 0.491354    
## Q118892.fctrYes               0.0707765  0.1248520   0.567 0.570793    
## Q119334.fctrNo               -0.1277239  0.1366822  -0.934 0.350067    
## Q119334.fctrYes              -0.1034233  0.1332186  -0.776 0.437547    
## Q119650.fctrGiving           -0.1249624  0.1415136  -0.883 0.377214    
## Q119650.fctrReceiving        -0.0142925  0.1582289  -0.090 0.928026    
## Q119851.fctrNo               -0.1811673  0.1630867  -1.111 0.266626    
## Q119851.fctrYes              -0.0180694  0.1622614  -0.111 0.911331    
## Q120012.fctrNo                0.0637899  0.1620091   0.394 0.693771    
## Q120012.fctrYes               0.1659133  0.1607887   1.032 0.302132    
## Q120014.fctrNo                0.0127211  0.1505837   0.084 0.932676    
## Q120014.fctrYes              -0.1240397  0.1429483  -0.868 0.385546    
## `Q120194.fctrStudy first`     0.3176012  0.1386330   2.291 0.021966 *  
## `Q120194.fctrTry first`       0.2164111  0.1440655   1.502 0.133053    
## Q120379.fctrNo               -0.0771023  0.1521128  -0.507 0.612242    
## Q120379.fctrYes               0.2205305  0.1505211   1.465 0.142890    
## Q120472.fctrArt              -0.0612904  0.1547468  -0.396 0.692054    
## Q120472.fctrScience          -0.1324117  0.1443147  -0.918 0.358870    
## Q120650.fctrNo               -0.0517376  0.1965069  -0.263 0.792330    
## Q120650.fctrYes              -0.1897041  0.1440182  -1.317 0.187764    
## Q120978.fctrNo                0.0561185  0.1581554   0.355 0.722716    
## Q120978.fctrYes               0.0607749  0.1543980   0.394 0.693858    
## Q121011.fctrNo                0.1730077  0.1586607   1.090 0.275526    
## Q121011.fctrYes               0.1459248  0.1562375   0.934 0.350307    
## Q121699.fctrNo                0.3917629  0.2430736   1.612 0.107026    
## Q121699.fctrYes               0.5626445  0.2341963   2.402 0.016286 *  
## Q121700.fctrNo               -0.4484119  0.2365117  -1.896 0.057968 .  
## Q121700.fctrYes              -0.3703624  0.2552292  -1.451 0.146753    
## Q122120.fctrNo               -0.0470544  0.1382689  -0.340 0.733622    
## Q122120.fctrYes              -0.1324785  0.1520012  -0.872 0.383447    
## Q122769.fctrNo               -0.0711802  0.2106482  -0.338 0.735431    
## Q122769.fctrYes              -0.0706567  0.2137226  -0.331 0.740947    
## Q122770.fctrNo                0.1791901  0.2564230   0.699 0.484673    
## Q122770.fctrYes               0.1582678  0.2530170   0.626 0.531628    
## Q122771.fctrPc               -0.2038295  0.2347224  -0.868 0.385183    
## Q122771.fctrPt               -0.4152093  0.2487436  -1.669 0.095073 .  
## Q123464.fctrNo               -0.0806655  0.1597095  -0.505 0.613505    
## Q123464.fctrYes               0.0799193  0.2336059   0.342 0.732267    
## Q123621.fctrNo               -0.0397879  0.1651916  -0.241 0.809665    
## Q123621.fctrYes              -0.0171433  0.1699050  -0.101 0.919630    
## Q124122.fctrNo               -0.0544581  0.1363018  -0.400 0.689495    
## Q124122.fctrYes               0.1016767  0.1309076   0.777 0.437332    
## Q124742.fctrNo                0.1625807  0.1045439   1.555 0.119912    
## Q124742.fctrYes               0.0065147  0.1207972   0.054 0.956990    
## Q96024.fctrNo                 0.0891299  0.1331683   0.669 0.503303    
## Q96024.fctrYes                0.0197989  0.1242295   0.159 0.873375    
## `Q98059.fctrOnly-child`      -0.0061363  0.2480548  -0.025 0.980264    
## Q98059.fctrYes                0.2800269  0.2075198   1.349 0.177209    
## Q98078.fctrNo                -0.0854335  0.1918980  -0.445 0.656173    
## Q98078.fctrYes               -0.1305647  0.1945453  -0.671 0.502139    
## Q98197.fctrNo                 0.4035689  0.1876747   2.150 0.031526 *  
## Q98197.fctrYes                0.0483067  0.1930565   0.250 0.802417    
## Q98578.fctrNo                -0.3931663  0.1556939  -2.525 0.011562 *  
## Q98578.fctrYes               -0.2830387  0.1628715  -1.738 0.082245 .  
## Q98869.fctrNo                 0.4837215  0.1689383   2.863 0.004192 ** 
## Q98869.fctrYes                0.0638411  0.1431705   0.446 0.655663    
## Q99480.fctrNo                 0.1611754  0.2028612   0.795 0.426898    
## Q99480.fctrYes               -0.1366070  0.1856640  -0.736 0.461867    
## Q99581.fctrNo                -0.2323245  0.2012039  -1.155 0.248225    
## Q99581.fctrYes               -0.1835486  0.2279284  -0.805 0.420652    
## Q99716.fctrNo                 0.2792082  0.1734253   1.610 0.107406    
## Q99716.fctrYes                0.1833083  0.2223343   0.824 0.409672    
## `Q99982.fctrCheck!`          -0.2313003  0.1929679  -1.199 0.230665    
## Q99982.fctrNope              -0.1569335  0.1958894  -0.801 0.423054    
## YOB.Age.fctr.L                0.4989675  0.1907924   2.615 0.008917 ** 
## YOB.Age.fctr.Q                0.2477063  0.1574487   1.573 0.115661    
## YOB.Age.fctr.C               -0.0613401  0.1363327  -0.450 0.652761    
## `YOB.Age.fctr^4`              0.2440100  0.1273923   1.915 0.055439 .  
## `YOB.Age.fctr^5`              0.0817560  0.1176802   0.695 0.487224    
## `YOB.Age.fctr^6`              0.1388637  0.1061805   1.308 0.190939    
## `YOB.Age.fctr^7`             -0.1783184  0.1008120  -1.769 0.076924 .  
## `YOB.Age.fctr^8`             -0.2096555  0.1037623  -2.021 0.043328 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 6150.3  on 4447  degrees of freedom
## Residual deviance: 5325.0  on 4215  degrees of freedom
## AIC: 5791
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "myfit_mdl: train diagnostics complete: 15.370000 secs"

##          Prediction
## Reference    R    D
##         R 1834  257
##         D 1375  982
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.330935e-01   2.851214e-01   6.187337e-01   6.472784e-01   5.299011e-01 
## AccuracyPValue  McnemarPValue 
##   4.069767e-44  2.800842e-168

##          Prediction
## Reference   R   D
##         R 483  43
##         D 439 155
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   5.696429e-01   1.717908e-01   5.400481e-01   5.988709e-01   5.303571e-01 
## AccuracyPValue  McnemarPValue 
##   4.539292e-03   2.260749e-72 
## [1] "myfit_mdl: predict complete: 25.911000 secs"
##               id
## 1 All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1               1                     12.416                  1.27
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6685598    0.6413199    0.6957997       0.2624767
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                   0.65       0.6920755        0.6049923
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.6187337             0.6472784     0.2065762
##   max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1       0.5989777    0.5380228    0.6599327       0.3371452
##   opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1                   0.75       0.6671271        0.5696429
##   max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1             0.5400481             0.5988709     0.1717908
##   max.AccuracySD.fit max.KappaSD.fit
## 1        0.007502422      0.01565638
## [1] "myfit_mdl: exit: 25.927000 secs"
# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
                label.minor = "preProc")
##                  label step_major step_minor label_minor     bgn     end
## 4   fit.models_1_All.X          1          3         glm 237.733 263.716
## 5 fit.models_1_preProc          1          4     preProc 263.716      NA
##   elapsed
## 4  25.983
## 5      NA
require(gdata)
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
## 
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
## 
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
## 
##     combine, first, last
## The following object is masked from 'package:stats':
## 
##     nobs
## The following object is masked from 'package:utils':
## 
##     object.size
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
                                                      "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
    fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
                                     glbObsFitOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
        print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
    
} else fitobs_df <- glbObsFit

for (prePr in glb_preproc_methods) {   
    # The operations are applied in this order: 
    #   Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
    
    ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
            id.prefix=mdl_id_pfx, 
            type=glb_model_type, tune.df=glbMdlTuneParams,
            trainControl.method="repeatedcv",
            trainControl.number=glb_rcv_n_folds,
            trainControl.repeats=glb_rcv_n_repeats,
            trainControl.classProbs = glb_is_classification,
            trainControl.summaryFunction = glbMdlMetricSummaryFn,
            train.metric = glbMdlMetricSummary, 
            train.maximize = glbMdlMetricMaximize,    
            train.method=method, train.preProcess=prePr)),
            indepVar=indepVar, rsp_var=glb_rsp_var, 
            fit_df=fitobs_df, OOB_df=glbObsOOB)
}            
    
    # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
    #   check NA coefficients & filter appropriate terms in indepVar
#     if (method == "glm") {
#         orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
#         orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
#         orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
#           require(car)
#           vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
#           # if vif errors out with "there are aliased coefficients in the model"
#               alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
#           print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
#           print(which.max(vif_orig_glm))
#           print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
#           glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
#           glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in%    grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
#           all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
#           cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
#           mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
#           subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
#         corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
#           which.max(abs_corxx_mtrx["S.T.tribun", ])
#           abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
#         step_glm <- step(orig_glm)
#     }
    # Since caret does not optimize rpart well
#     if (method == "rpart")
#         ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,        
#             n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))

# User specified
#   Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df

    # easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
#                         , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")

    # easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
#     ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
#     ,"prdline.my.fctr*biddable"
#     #,"prdline.my.fctr*startprice.log"
#     #,"prdline.my.fctr*startprice.diff"    
#     ,"prdline.my.fctr*condition.fctr"
#     ,"prdline.my.fctr*D.terms.post.stop.n"
#     #,"prdline.my.fctr*D.terms.post.stem.n"
#     ,"prdline.my.fctr*cellular.fctr"    
# #    ,"<feat1>:<feat2>"
#                                            )
# for (method in glbMdlMethods) {
#     ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
#                                 indepVar=indepVar,
#                                 model_type=glb_model_type,
#                                 rsp_var=glb_rsp_var,
#                                 fit_df=glbObsFit, OOB_df=glbObsOOB,
#                     n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
#     csm_mdl_id <- paste0(mdl_id, ".", method)
#     csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
#                                                                      method)]]);               print(head(csm_featsimp_df))
# }
###

# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))

#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]

    # User specified bivariate models
#     indepVar_lst <- list()
#     for (feat in setdiff(names(glbObsFit), 
#                          union(glb_rsp_var, glbFeatsExclude)))
#         indepVar_lst[["feat"]] <- feat

    # User specified combinatorial models
#     indepVar_lst <- list()
#     combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"), 
#                           <num_feats_to_choose>)
#     for (combn_ix in 1:ncol(combn_mtrx))
#         #print(combn_mtrx[, combn_ix])
#         indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
    
    # template for myfit_mdl
    #   rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
    #       only for OOB in trainControl ?
    
#     ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
#                             indepVar=indepVar,
#                             rsp_var=glb_rsp_var,
#                             fit_df=glbObsFit, OOB_df=glbObsOOB,
#                             n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
#                             model_loss_mtrx=glbMdlMetric_terms,
#                             model_summaryFunction=glbMdlMetricSummaryFn,
#                             model_metric=glbMdlMetricSummary,
#                             model_metric_maximize=glbMdlMetricMaximize)

# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)

# Non-caret models
#     rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var), 
#                                data=glbObsFit, #method="class", 
#                                control=rpart.control(cp=0.12),
#                            parms=list(loss=glbMdlMetric_terms))
#     print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
# 

print(glb_models_df)
##                                                              id
## MFO###myMFO_classfr                         MFO###myMFO_classfr
## Random###myrandom_classfr             Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  feats
## MFO###myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             .rnorm
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet           Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glmnet               Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glm                  Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##                                 max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr                           0                      0.386
## Random###myrandom_classfr                     0                      0.270
## Max.cor.Y.rcv.1X1###glmnet                    0                      0.790
## Max.cor.Y##rcv#rpart                          5                      1.566
## Interact.High.cor.Y##rcv#glmnet              25                      5.276
## Low.cor.X##rcv#glmnet                        25                     23.305
## All.X##rcv#glmnet                            25                     22.908
## All.X##rcv#glm                                1                     12.416
##                                 min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr                             0.003       0.5000000
## Random###myrandom_classfr                       0.002       0.4942483
## Max.cor.Y.rcv.1X1###glmnet                      0.062       0.5971118
## Max.cor.Y##rcv#rpart                            0.019       0.5971118
## Interact.High.cor.Y##rcv#glmnet                 0.355       0.6184781
## Low.cor.X##rcv#glmnet                           2.128       0.6534188
## All.X##rcv#glmnet                               2.054       0.6534188
## All.X##rcv#glm                                  1.270       0.6685598
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr                0.0000000    1.0000000       0.5000000
## Random###myrandom_classfr          0.4619799    0.5265168       0.5073101
## Max.cor.Y.rcv.1X1###glmnet         0.5480631    0.6461604       0.3580613
## Max.cor.Y##rcv#rpart               0.5480631    0.6461604       0.3676308
## Interact.High.cor.Y##rcv#glmnet    0.5958871    0.6410692       0.3319465
## Low.cor.X##rcv#glmnet              0.5872788    0.7195588       0.2783723
## All.X##rcv#glmnet                  0.5872788    0.7195588       0.2783723
## All.X##rcv#glm                     0.6413199    0.6957997       0.2624767
##                                 opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                               0.50       0.6395473
## Random###myrandom_classfr                         0.55       0.6395473
## Max.cor.Y.rcv.1X1###glmnet                        0.60       0.6720662
## Max.cor.Y##rcv#rpart                              0.55       0.6720662
## Interact.High.cor.Y##rcv#glmnet                   0.65       0.6727114
## Low.cor.X##rcv#glmnet                             0.60       0.6872903
## All.X##rcv#glmnet                                 0.60       0.6872903
## All.X##rcv#glm                                    0.65       0.6920755
##                                 max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr                    0.4700989             0.4553427
## Random###myrandom_classfr              0.4700989             0.4553427
## Max.cor.Y.rcv.1X1###glmnet             0.5721673             0.5574714
## Max.cor.Y##rcv#rpart                   0.6000450             0.5574714
## Interact.High.cor.Y##rcv#glmnet        0.6058167             0.5633390
## Low.cor.X##rcv#glmnet                  0.6254518             0.6083184
## All.X##rcv#glmnet                      0.6254518             0.6083184
## All.X##rcv#glm                         0.6049923             0.6187337
##                                 max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr                         0.4848945     0.0000000
## Random###myrandom_classfr                   0.4848945     0.0000000
## Max.cor.Y.rcv.1X1###glmnet                  0.5867683     0.1772539
## Max.cor.Y##rcv#rpart                        0.5867683     0.1947896
## Interact.High.cor.Y##rcv#glmnet             0.5925837     0.2088694
## Low.cor.X##rcv#glmnet                       0.6370239     0.2446067
## All.X##rcv#glmnet                           0.6370239     0.2446067
## All.X##rcv#glm                              0.6472784     0.2065762
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr                   0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr             0.5235690    0.5000000    0.5471380
## Max.cor.Y.rcv.1X1###glmnet            0.5896897    0.5228137    0.6565657
## Max.cor.Y##rcv#rpart                  0.5896897    0.5228137    0.6565657
## Interact.High.cor.Y##rcv#glmnet       0.6031353    0.5665399    0.6397306
## Low.cor.X##rcv#glmnet                 0.6261026    0.5266160    0.7255892
## All.X##rcv#glmnet                     0.6261026    0.5266160    0.7255892
## All.X##rcv#glm                        0.5989777    0.5380228    0.6599327
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr                   0.5000000                   0.50
## Random###myrandom_classfr             0.5191202                   0.55
## Max.cor.Y.rcv.1X1###glmnet            0.3658672                   0.60
## Max.cor.Y##rcv#rpart                  0.3774772                   0.55
## Interact.High.cor.Y##rcv#glmnet       0.3571392                   0.65
## Low.cor.X##rcv#glmnet                 0.3157430                   0.65
## All.X##rcv#glmnet                     0.3157430                   0.65
## All.X##rcv#glm                        0.3371452                   0.75
##                                 max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr                   0.6391252        0.4696429
## Random###myrandom_classfr             0.6391252        0.4696429
## Max.cor.Y.rcv.1X1###glmnet            0.6643789        0.5633929
## Max.cor.Y##rcv#rpart                  0.6643789        0.5633929
## Interact.High.cor.Y##rcv#glmnet       0.6680556        0.5732143
## Low.cor.X##rcv#glmnet                 0.6699507        0.5812500
## All.X##rcv#glmnet                     0.6699507        0.5812500
## All.X##rcv#glm                        0.6671271        0.5696429
##                                 max.AccuracyLower.OOB
## MFO###myMFO_classfr                         0.4400805
## Random###myrandom_classfr                   0.4400805
## Max.cor.Y.rcv.1X1###glmnet                  0.5337655
## Max.cor.Y##rcv#rpart                        0.5337655
## Interact.High.cor.Y##rcv#glmnet             0.5436402
## Low.cor.X##rcv#glmnet                       0.5517282
## All.X##rcv#glmnet                           0.5517282
## All.X##rcv#glm                              0.5400481
##                                 max.AccuracyUpper.OOB max.Kappa.OOB
## MFO###myMFO_classfr                         0.4993651     0.0000000
## Random###myrandom_classfr                   0.4993651     0.0000000
## Max.cor.Y.rcv.1X1###glmnet                  0.5926864     0.1605510
## Max.cor.Y##rcv#rpart                        0.5926864     0.1605510
## Interact.High.cor.Y##rcv#glmnet             0.6024028     0.1779779
## Low.cor.X##rcv#glmnet                       0.6103439     0.1918521
## All.X##rcv#glmnet                           0.6103439     0.1918521
## All.X##rcv#glm                              0.5988709     0.1717908
##                                 max.AccuracySD.fit max.KappaSD.fit
## MFO###myMFO_classfr                             NA              NA
## Random###myrandom_classfr                       NA              NA
## Max.cor.Y.rcv.1X1###glmnet                      NA              NA
## Max.cor.Y##rcv#rpart                   0.012403504      0.02559319
## Interact.High.cor.Y##rcv#glmnet        0.013121299      0.02732571
## Low.cor.X##rcv#glmnet                  0.012904832      0.02676153
## All.X##rcv#glmnet                      0.012904832      0.02676153
## All.X##rcv#glm                         0.007502422      0.01565638
rm(ret_lst)
fit.models_1_chunk_df <- 
    myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
                label.minor = "teardown")
##                  label step_major step_minor label_minor     bgn     end
## 5 fit.models_1_preProc          1          4     preProc 263.716 264.665
## 6     fit.models_1_end          1          5    teardown 264.665      NA
##   elapsed
## 5   0.949
## 6      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
##        label step_major step_minor label_minor     bgn     end elapsed
## 5 fit.models          4          1           1 198.236 264.675  66.439
## 6 fit.models          4          2           2 264.676      NA      NA
fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0       setup 268.776  NA      NA
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
    plt_models_df[, sub("min.", "inv.", var)] <- 
        #ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
        1.0 / plt_models_df[, var]
    plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
##                                                              id
## MFO###myMFO_classfr                         MFO###myMFO_classfr
## Random###myrandom_classfr             Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet           Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart                       Max.cor.Y##rcv#rpart
## Interact.High.cor.Y##rcv#glmnet Interact.High.cor.Y##rcv#glmnet
## Low.cor.X##rcv#glmnet                     Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet                             All.X##rcv#glmnet
## All.X##rcv#glm                                   All.X##rcv#glm
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  feats
## MFO###myMFO_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             .rnorm
## Random###myrandom_classfr                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       .rnorm
## Max.cor.Y.rcv.1X1###glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Q109244.fctr,Gender.fctr
## Max.cor.Y##rcv#rpart                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Q109244.fctr,Gender.fctr
## Interact.High.cor.Y##rcv#glmnet                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Q109244.fctr,Gender.fctr,Q109244.fctr:Q99480.fctr,Q109244.fctr:Q98078.fctr,Q109244.fctr:Q100689.fctr,Q109244.fctr:Q108855.fctr,Q109244.fctr:Q123621.fctr,Q109244.fctr:Q122771.fctr,Q109244.fctr:Q120472.fctr,Q109244.fctr:Q106272.fctr
## Low.cor.X##rcv#glmnet           Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glmnet               Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
## All.X##rcv#glm                  Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##                                 max.nTuningRuns max.AUCpROC.fit
## MFO###myMFO_classfr                           0       0.5000000
## Random###myrandom_classfr                     0       0.4942483
## Max.cor.Y.rcv.1X1###glmnet                    0       0.5971118
## Max.cor.Y##rcv#rpart                          5       0.5971118
## Interact.High.cor.Y##rcv#glmnet              25       0.6184781
## Low.cor.X##rcv#glmnet                        25       0.6534188
## All.X##rcv#glmnet                            25       0.6534188
## All.X##rcv#glm                                1       0.6685598
##                                 max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr                0.0000000    1.0000000       0.5000000
## Random###myrandom_classfr          0.4619799    0.5265168       0.5073101
## Max.cor.Y.rcv.1X1###glmnet         0.5480631    0.6461604       0.3580613
## Max.cor.Y##rcv#rpart               0.5480631    0.6461604       0.3676308
## Interact.High.cor.Y##rcv#glmnet    0.5958871    0.6410692       0.3319465
## Low.cor.X##rcv#glmnet              0.5872788    0.7195588       0.2783723
## All.X##rcv#glmnet                  0.5872788    0.7195588       0.2783723
## All.X##rcv#glm                     0.6413199    0.6957997       0.2624767
##                                 opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr                               0.50       0.6395473
## Random###myrandom_classfr                         0.55       0.6395473
## Max.cor.Y.rcv.1X1###glmnet                        0.60       0.6720662
## Max.cor.Y##rcv#rpart                              0.55       0.6720662
## Interact.High.cor.Y##rcv#glmnet                   0.65       0.6727114
## Low.cor.X##rcv#glmnet                             0.60       0.6872903
## All.X##rcv#glmnet                                 0.60       0.6872903
## All.X##rcv#glm                                    0.65       0.6920755
##                                 max.Accuracy.fit max.Kappa.fit
## MFO###myMFO_classfr                    0.4700989     0.0000000
## Random###myrandom_classfr              0.4700989     0.0000000
## Max.cor.Y.rcv.1X1###glmnet             0.5721673     0.1772539
## Max.cor.Y##rcv#rpart                   0.6000450     0.1947896
## Interact.High.cor.Y##rcv#glmnet        0.6058167     0.2088694
## Low.cor.X##rcv#glmnet                  0.6254518     0.2446067
## All.X##rcv#glmnet                      0.6254518     0.2446067
## All.X##rcv#glm                         0.6049923     0.2065762
##                                 max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr                   0.5000000    0.0000000    1.0000000
## Random###myrandom_classfr             0.5235690    0.5000000    0.5471380
## Max.cor.Y.rcv.1X1###glmnet            0.5896897    0.5228137    0.6565657
## Max.cor.Y##rcv#rpart                  0.5896897    0.5228137    0.6565657
## Interact.High.cor.Y##rcv#glmnet       0.6031353    0.5665399    0.6397306
## Low.cor.X##rcv#glmnet                 0.6261026    0.5266160    0.7255892
## All.X##rcv#glmnet                     0.6261026    0.5266160    0.7255892
## All.X##rcv#glm                        0.5989777    0.5380228    0.6599327
##                                 max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr                   0.5000000                   0.50
## Random###myrandom_classfr             0.5191202                   0.55
## Max.cor.Y.rcv.1X1###glmnet            0.3658672                   0.60
## Max.cor.Y##rcv#rpart                  0.3774772                   0.55
## Interact.High.cor.Y##rcv#glmnet       0.3571392                   0.65
## Low.cor.X##rcv#glmnet                 0.3157430                   0.65
## All.X##rcv#glmnet                     0.3157430                   0.65
## All.X##rcv#glm                        0.3371452                   0.75
##                                 max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr                   0.6391252        0.4696429
## Random###myrandom_classfr             0.6391252        0.4696429
## Max.cor.Y.rcv.1X1###glmnet            0.6643789        0.5633929
## Max.cor.Y##rcv#rpart                  0.6643789        0.5633929
## Interact.High.cor.Y##rcv#glmnet       0.6680556        0.5732143
## Low.cor.X##rcv#glmnet                 0.6699507        0.5812500
## All.X##rcv#glmnet                     0.6699507        0.5812500
## All.X##rcv#glm                        0.6671271        0.5696429
##                                 max.Kappa.OOB inv.elapsedtime.everything
## MFO###myMFO_classfr                 0.0000000                 2.59067358
## Random###myrandom_classfr           0.0000000                 3.70370370
## Max.cor.Y.rcv.1X1###glmnet          0.1605510                 1.26582278
## Max.cor.Y##rcv#rpart                0.1605510                 0.63856960
## Interact.High.cor.Y##rcv#glmnet     0.1779779                 0.18953753
## Low.cor.X##rcv#glmnet               0.1918521                 0.04290925
## All.X##rcv#glmnet                   0.1918521                 0.04365287
## All.X##rcv#glm                      0.1717908                 0.08054124
##                                 inv.elapsedtime.final
## MFO###myMFO_classfr                       333.3333333
## Random###myrandom_classfr                 500.0000000
## Max.cor.Y.rcv.1X1###glmnet                 16.1290323
## Max.cor.Y##rcv#rpart                       52.6315789
## Interact.High.cor.Y##rcv#glmnet             2.8169014
## Low.cor.X##rcv#glmnet                       0.4699248
## All.X##rcv#glmnet                           0.4868549
## All.X##rcv#glm                              0.7874016
# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df, 
#         !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))

# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df, 
                max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
                min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
    # Does CI alredy exist ?
    var_components <- unlist(strsplit(var, "SD"))
    varActul <- paste0(var_components[1],          var_components[2])
    varUpper <- paste0(var_components[1], "Upper", var_components[2])
    varLower <- paste0(var_components[1], "Lower", var_components[2])
    if (varUpper %in% names(glb_models_df)) {
        warning(varUpper, " already exists in glb_models_df")
        # Assuming Lower also exists
        next
    }    
    print(sprintf("var:%s", var))
    # CI is dependent on sample size in t distribution; df=n-1
    glb_models_df[, varUpper] <- glb_models_df[, varActul] + 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
    glb_models_df[, varLower] <- glb_models_df[, varActul] - 
        glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
    var_components <- unlist(strsplit(var, "Upper"))
    col_name <- unlist(paste(var_components, collapse=""))
    plt_models_df[, col_name] <- glb_models_df[, col_name]
    for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
        pltCI_models_df[, name] <- glb_models_df[, name]
}

build_statsCI_data <- function(plt_models_df) {
    mltd_models_df <- melt(plt_models_df, id.vars="id")
    mltd_models_df$data <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) tail(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), "[.]")), 1))
    mltd_models_df$label <- sapply(1:nrow(mltd_models_df), 
        function(row_ix) head(unlist(strsplit(as.character(
            mltd_models_df[row_ix, "variable"]), 
            paste0(".", mltd_models_df[row_ix, "data"]))), 1))
    #print(mltd_models_df)
    
    return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)

mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
    for (type in c("Upper", "Lower")) {
        if (length(var_components <- unlist(strsplit(
                as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
            #print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
            mltdCI_models_df[row_ix, "label"] <- var_components[1]
            mltdCI_models_df[row_ix, "data"] <- 
                unlist(strsplit(var_components[2], "[.]"))[2]
            mltdCI_models_df[row_ix, "type"] <- type
            break
        }
    }    
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable), 
                            timevar="type", 
        idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")), 
                            direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)

# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
    for (type in unique(mltd_models_df$data)) {
        var_type <- paste0(var, ".", type)
        # if this data is already present, next
        if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
                                       sep=".")))
            next
        #print(sprintf("var_type:%s", var_type))
        goback_vars <- c(goback_vars, var_type)
    }
}

if (length(goback_vars) > 0) {
    mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
    mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}

# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")], 
#                         all.x=TRUE)

png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") + 
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") + 
        geom_errorbar(data=mrgdCI_models_df, 
            mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) + 
          facet_grid(label ~ data, scales="free") + 
          theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dev.off()
## quartz_off_screen 
##                 2
print(gp)
## Warning: Removed 4 rows containing missing values (geom_errorbar).

dsp_models_cols <- c("id", 
                    glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
                    grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE)) 
# if (glb_is_classification && glb_is_binomial) 
#     dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])
##                                id max.Accuracy.OOB max.AUCROCR.OOB
## 6           Low.cor.X##rcv#glmnet        0.5812500       0.3157430
## 7               All.X##rcv#glmnet        0.5812500       0.3157430
## 5 Interact.High.cor.Y##rcv#glmnet        0.5732143       0.3571392
## 8                  All.X##rcv#glm        0.5696429       0.3371452
## 4            Max.cor.Y##rcv#rpart        0.5633929       0.3774772
## 3      Max.cor.Y.rcv.1X1###glmnet        0.5633929       0.3658672
## 2       Random###myrandom_classfr        0.4696429       0.5191202
## 1             MFO###myMFO_classfr        0.4696429       0.5000000
##   max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 6       0.6261026        0.6254518                   0.60
## 7       0.6261026        0.6254518                   0.60
## 5       0.6031353        0.6058167                   0.65
## 8       0.5989777        0.6049923                   0.65
## 4       0.5896897        0.6000450                   0.55
## 3       0.5896897        0.5721673                   0.60
## 2       0.5235690        0.4700989                   0.55
## 1       0.5000000        0.4700989                   0.50
##   opt.prob.threshold.OOB
## 6                   0.65
## 7                   0.65
## 5                   0.65
## 8                   0.75
## 4                   0.55
## 3                   0.60
## 2                   0.55
## 1                   0.50
# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit - 
##     opt.prob.threshold.OOB
## <environment: 0x7fbc238a55b8>
print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))
## [1] "Best model id: Low.cor.X##rcv#glmnet"
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
    mdl <- glb_models_lst[[mdl_id]]
    
    clmnNames <- mygetPredictIds(rsp_var, mdl_id)
    predct_var_name <- clmnNames$value        
    predct_prob_var_name <- clmnNames$prob
    predct_accurate_var_name <- clmnNames$is.acc
    predct_error_var_name <- clmnNames$err
    predct_erabs_var_name <- clmnNames$err.abs

    if (glb_is_regression) {
        df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
                  facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
        if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="auto"))
        if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
                  #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
                  stat_smooth(method="glm"))
        
        df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }

    if (glb_is_classification && glb_is_binomial) {
        prob_threshold <- glb_models_df[glb_models_df$id == mdl_id, 
                                        "opt.prob.threshold.OOB"]
        if (is.null(prob_threshold) || is.na(prob_threshold)) {
            warning("Using default probability threshold: ", prob_threshold_def)
            if (is.null(prob_threshold <- prob_threshold_def))
                stop("Default probability threshold is NULL")
        }
        
        df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
        df[, predct_var_name] <- 
                factor(levels(df[, glb_rsp_var])[
                    (df[, predct_prob_var_name] >=
                        prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
    
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) + 
#                   facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))

        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
#         if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="auto"))
#         if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) + 
#                   #facet_wrap(reformulate(glbFeatsCategory), scales = "free") + 
#                   stat_smooth(method="glm"))
        
        # if prediction is a TP (true +ve), measure distance from 1.0
        tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
        #rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a TN (true -ve), measure distance from 0.0
        tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
        #rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FP (flse +ve), measure distance from 0.0
        fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
        df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
        #rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
        
        # if prediction is a FN (flse -ve), measure distance from 1.0
        fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
                    (df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
        df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
        #rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]

        
        if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
        
        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
    }    
    
    if (glb_is_classification && !glb_is_binomial) {
        df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
        probCls <- predict(mdl, newdata = df, type = "prob")        
        df[, predct_prob_var_name] <- NA
        for (cls in names(probCls)) {
            mask <- (df[, predct_var_name] == cls)
            df[mask, predct_prob_var_name] <- probCls[mask, cls]
        }    
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            fill_col_name = predct_var_name))
        if (verbose) print(myplot_histogram(df, predct_prob_var_name, 
                                            facet_frmla = paste0("~", glb_rsp_var)))
        
        df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
        
        # if prediction is erroneous, measure predicted class prob from actual class prob
        df[, predct_erabs_var_name] <- 0
        for (cls in names(probCls)) {
            mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
            df[mask, predct_erabs_var_name] <- probCls[mask, cls]
        }    

        df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])        
    }

    return(df)
}    

#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df    

myget_category_stats <- function(obs_df, mdl_id, label) {
    require(dplyr)
    require(lazyeval)
    
    predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value        
    predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
    
    if (!predct_var_name %in% names(obs_df))
        obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
    
    tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var, 
                             predct_var_name, predct_error_var_name)]
#     tmp_obs_df <- obs_df %>%
#         dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name) 
    #dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
    names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
    
    ret_ctgry_df <- tmp_obs_df %>%
        dplyr::group_by_(glbFeatsCategory) %>%
        dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)), 
            interp(~sum(var), var=as.name(paste0("err.abs.", label))), 
            interp(~mean(var), var=as.name(paste0("err.abs.", label))),
            interp(~n()))
    names(ret_ctgry_df) <- c(glbFeatsCategory, 
                             #paste0(glb_rsp_var, ".abs.", label, ".sum"),
                             paste0("err.abs.", label, ".sum"),                             
                             paste0("err.abs.", label, ".mean"), 
                             paste0(".n.", label))
    ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
    #colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
    
    return(ret_ctgry_df)    
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))

if (!is.null(glb_mdl_ensemble)) {
    fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df, 
                            paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE, 
                                                label.minor = "ensemble")
    
    mdl_id_pfx <- "Ensemble"

    if (#(glb_is_regression) | 
        ((glb_is_classification) & (!glb_is_binomial)))
        stop("Ensemble models not implemented yet for multinomial classification")
    
    mygetEnsembleAutoMdlIds <- function() {
        tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
        row.names(tmp_models_df) <- tmp_models_df$id
        mdl_threshold_pos <- 
            min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
        mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
        return(mdlIds[!grepl("Ensemble", mdlIds)])
    }
    
    if (glb_mdl_ensemble == "auto") {
        glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
        mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")        
    } else if (grepl("^%<d-%", glb_mdl_ensemble)) {
        glb_mdl_ensemble <- eval(parse(text =
                        str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
    }
    
    for (mdl_id in glb_mdl_ensemble) {
        if (!(mdl_id %in% names(glb_models_lst))) {
            warning("Model ", mdl_id, " in glb_model_ensemble not found !")
            next
        }
        glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
        glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
    }
    
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
    
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
    
    ### bid0_sp
    #  Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
    #  old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
    #  RFE only ;       models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
    #  RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
    #  RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
    #  RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
    #  RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
    #  RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    #  RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
    ### bid0_sp
    ### bid1_sp
    # "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
    # "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
    ### bid1_sp

    indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
    if (glb_is_classification)
        indepVar <- paste(indepVar, ".prob", sep = "")
    # Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
    indepVar <- intersect(indepVar, names(glbObsFit))
    
#     indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
#     if (glb_is_regression)
#         indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
#     if (glb_is_classification && glb_is_binomial)
#         indepVar <- grep("prob$", indepVar, value=TRUE) else
#         indepVar <- indepVar[!grepl("err$", indepVar)]

    #rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
    
    for (method in c("glm", "glmnet")) {
        for (trainControlMethod in 
             c("boot", "boot632", "cv", "repeatedcv"
               #, "LOOCV" # tuneLength * nrow(fitDF)
               , "LGOCV", "adaptive_cv"
               #, "adaptive_boot"  #error: adaptive$min should be less than 3 
               #, "adaptive_LGOCV" #error: adaptive$min should be less than 3 
               )) {
            #sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
            #glb_models_df <- sav_models_df; print(glb_models_df$id)
                
            if ((method == "glm") && (trainControlMethod != "repeatedcv"))
                # glm used only to identify outliers
                next
            
            ret_lst <- myfit_mdl(
                mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod), 
                    type = glb_model_type, tune.df = NULL,
                    trainControl.method = trainControlMethod,
                    trainControl.number = glb_rcv_n_folds,
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = glbObsFit, OOB_df = glbObsOOB)
        }
    }
    dsp_models_df <- get_dsp_models_df()
}

if (is.null(glbMdlSelId)) 
    glbMdlSelId <- dsp_models_df[1, "id"] else 
    print(sprintf("User specified selection: %s", glbMdlSelId))   
## [1] "User specified selection: All.X##rcv#glmnet"
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])

##             Length Class      Mode     
## a0             88  -none-     numeric  
## beta        20416  dgCMatrix  S4       
## df             88  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         88  -none-     numeric  
## dev.ratio      88  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        232  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##               (Intercept)                Edn.fctr^4 
##              0.1920840842             -0.1005414034 
##                Edn.fctr^6                Edn.fctr^7 
##              0.0334860119              0.0710425863 
##              Gender.fctrM             Hhold.fctrMKy 
##             -0.0794064511             -0.1371778566 
##             Hhold.fctrPKn             Hhold.fctrSKn 
##              0.4997234624              0.0176790778 
##             Hhold.fctrSKy             Income.fctr.Q 
##              0.1011680811             -0.0816169876 
##             Income.fctr.C             Income.fctr^4 
##             -0.1216519159             -0.0166494558 
##            Q100010.fctrNo           Q100680.fctrYes 
##              0.0094368536              0.0029292851 
##           Q100689.fctrYes     Q101162.fctrPessimist 
##              0.0884714668             -0.0071088785 
##           Q101163.fctrDad           Q101163.fctrMom 
##             -0.1073473633              0.0759596110 
##            Q101596.fctrNo           Q102687.fctrYes 
##             -0.0036990765              0.0309988067 
##            Q104996.fctrNo           Q104996.fctrYes 
##             -0.0444993808              0.0096111358 
##           Q105655.fctrYes            Q105840.fctrNo 
##             -0.0403102885             -0.0039386021 
##            Q106042.fctrNo           Q106272.fctrYes 
##             -0.0351407304             -0.0370732762 
##            Q106389.fctrNo            Q106997.fctrGr 
##             -0.0642024577             -0.0433734852 
##            Q106997.fctrYy           Q107491.fctrYes 
##              0.0814150590              0.0165630338 
##        Q108342.fctrOnline          Q108855.fctrYes! 
##              0.0631187924             -0.0426051502 
## Q108950.fctrRisk-friendly            Q109244.fctrNo 
##              0.0371748796             -0.3676230271 
##           Q109244.fctrYes           Q110740.fctrMac 
##              0.7526090401              0.0158364523 
##            Q110740.fctrPC           Q111220.fctrYes 
##             -0.0902880024              0.0957826931 
##           Q111848.fctrYes            Q112478.fctrNo 
##              0.0250589583             -0.0652435089 
##            Q113181.fctrNo           Q113181.fctrYes 
##              0.0925300327             -0.1002574917 
##           Q113992.fctrYes            Q115390.fctrNo 
##              0.0125173544             -0.0801709946 
##           Q115390.fctrYes            Q115611.fctrNo 
##              0.0226179572              0.1209408406 
##           Q115611.fctrYes            Q115899.fctrCs 
##             -0.3209136716              0.0774284066 
##            Q115899.fctrMe          Q116197.fctrA.M. 
##             -0.0123327062             -0.0248274337 
##         Q116881.fctrHappy         Q116881.fctrRight 
##              0.0742138891             -0.1383231722 
##            Q116953.fctrNo           Q116953.fctrYes 
##             -0.0348182328              0.0562055276 
##    Q117186.fctrHot headed            Q118232.fctrId 
##             -0.0145383547              0.1136211170 
##            Q118233.fctrNo           Q118233.fctrYes 
##             -0.0170028152              0.0113463031 
##        Q119650.fctrGiving            Q119851.fctrNo 
##             -0.0007454134             -0.1103781004 
##           Q119851.fctrYes           Q120012.fctrYes 
##              0.0120497740              0.0366728192 
##            Q120014.fctrNo           Q120014.fctrYes 
##              0.0280032695             -0.0299491592 
##   Q120194.fctrStudy first            Q120379.fctrNo 
##              0.0598036091             -0.0497973859 
##           Q120379.fctrYes       Q120472.fctrScience 
##              0.1111013195             -0.0359739528 
##           Q120650.fctrYes            Q121699.fctrNo 
##             -0.0249600547             -0.0544691763 
##           Q121699.fctrYes            Q121700.fctrNo 
##              0.0374055344             -0.0072164530 
##           Q121700.fctrYes           Q122120.fctrYes 
##              0.0163285151             -0.0198345229 
##            Q122771.fctrPt            Q123464.fctrNo 
##             -0.1085074600             -0.0188472361 
##            Q124122.fctrNo           Q124122.fctrYes 
##             -0.0315353659              0.0006062715 
##            Q124742.fctrNo             Q96024.fctrNo 
##              0.0312573363              0.0189226203 
##     Q98059.fctrOnly-child            Q98059.fctrYes 
##             -0.0023811587              0.0653704563 
##             Q98197.fctrNo            Q98197.fctrYes 
##              0.1793569818             -0.0826453929 
##             Q98578.fctrNo             Q98869.fctrNo 
##             -0.0365491345              0.2587862534 
##             Q99480.fctrNo            Q99480.fctrYes 
##              0.1316394499             -0.0381190876 
##            YOB.Age.fctr.L            YOB.Age.fctr.Q 
##              0.1159818986              0.0236491601 
##            YOB.Age.fctr^4            YOB.Age.fctr^6 
##              0.0449093485              0.0053748587 
##            YOB.Age.fctr^7            YOB.Age.fctr^8 
##             -0.0412125185             -0.0632525584 
## [1] "max lambda < lambdaOpt:"
##                (Intercept)                 Edn.fctr^4 
##                0.190242606               -0.113726050 
##                 Edn.fctr^6                 Edn.fctr^7 
##                0.039985577                0.076765149 
##               Gender.fctrM              Hhold.fctrMKy 
##               -0.079499199               -0.139799243 
##              Hhold.fctrPKn              Hhold.fctrSKn 
##                0.516895044                0.023441414 
##              Hhold.fctrSKy              Income.fctr.Q 
##                0.112262855               -0.086764305 
##              Income.fctr.C              Income.fctr^4 
##               -0.131008068               -0.022207230 
##              Income.fctr^6             Q100010.fctrNo 
##                0.005184510                0.015575737 
##            Q100680.fctrYes            Q100689.fctrYes 
##                0.005695513                0.096910592 
##      Q101162.fctrPessimist            Q101163.fctrDad 
##               -0.009462485               -0.112012131 
##            Q101163.fctrMom             Q101596.fctrNo 
##                0.077204753               -0.011035252 
##            Q102687.fctrYes             Q103293.fctrNo 
##                0.036506262               -0.003992889 
##             Q104996.fctrNo            Q104996.fctrYes 
##               -0.046964218                0.013628231 
##            Q105655.fctrYes             Q105840.fctrNo 
##               -0.045689342               -0.004398376 
##             Q106042.fctrNo            Q106272.fctrYes 
##               -0.037204139               -0.042078733 
##             Q106389.fctrNo             Q106997.fctrGr 
##               -0.069884472               -0.045946207 
##             Q106997.fctrYy            Q107491.fctrYes 
##                0.087529010                0.022505522 
##         Q108342.fctrOnline           Q108855.fctrYes! 
##                0.068972019               -0.048326214 
##  Q108950.fctrRisk-friendly             Q109244.fctrNo 
##                0.042397185               -0.374416559 
##            Q109244.fctrYes            Q110740.fctrMac 
##                0.763571824                0.016457805 
##             Q110740.fctrPC            Q111220.fctrYes 
##               -0.095759349                0.102165768 
##            Q111848.fctrYes            Q112270.fctrYes 
##                0.029246498                0.002532131 
##             Q112478.fctrNo             Q113181.fctrNo 
##               -0.071674308                0.094659726 
##            Q113181.fctrYes            Q113992.fctrYes 
##               -0.101925125                0.018758347 
##            Q114152.fctrYes     Q114386.fctrMysterious 
##                0.001215794                0.005550234 
##             Q115390.fctrNo            Q115390.fctrYes 
##               -0.085128214                0.024943706 
##             Q115602.fctrNo             Q115611.fctrNo 
##               -0.006129422                0.121429244 
##            Q115611.fctrYes             Q115899.fctrCs 
##               -0.328446834                0.082573783 
##             Q115899.fctrMe           Q116197.fctrA.M. 
##               -0.012353330               -0.032450966 
##          Q116881.fctrHappy          Q116881.fctrRight 
##                0.078603456               -0.141878310 
##             Q116953.fctrNo            Q116953.fctrYes 
##               -0.036656763                0.063262470 
##     Q117186.fctrHot headed Q117193.fctrStandard hours 
##               -0.019710980               -0.002981009 
##             Q118232.fctrId             Q118233.fctrNo 
##                0.120887721               -0.021333073 
##            Q118233.fctrYes         Q119650.fctrGiving 
##                0.014400900               -0.006146699 
##             Q119851.fctrNo            Q119851.fctrYes 
##               -0.113773685                0.013486484 
##            Q120012.fctrYes             Q120014.fctrNo 
##                0.040890779                0.032195937 
##            Q120014.fctrYes    Q120194.fctrStudy first 
##               -0.032533254                0.065256829 
##             Q120379.fctrNo            Q120379.fctrYes 
##               -0.050149716                0.118837105 
##        Q120472.fctrScience            Q120650.fctrYes 
##               -0.037775351               -0.030467057 
##             Q121699.fctrNo            Q121699.fctrYes 
##               -0.051633489                0.044689201 
##             Q121700.fctrNo            Q121700.fctrYes 
##               -0.011071443                0.016970195 
##            Q122120.fctrYes             Q122771.fctrPt 
##               -0.024872502               -0.115816557 
##             Q123464.fctrNo             Q124122.fctrNo 
##               -0.024443028               -0.034722635 
##            Q124122.fctrYes             Q124742.fctrNo 
##                0.004874931                0.039013147 
##              Q96024.fctrNo      Q98059.fctrOnly-child 
##                0.023646239               -0.009596390 
##             Q98059.fctrYes              Q98197.fctrNo 
##                0.073367383                0.184718092 
##             Q98197.fctrYes              Q98578.fctrNo 
##               -0.083886484               -0.043820323 
##              Q98869.fctrNo              Q99480.fctrNo 
##                0.266626746                0.134742947 
##             Q99480.fctrYes             YOB.Age.fctr.L 
##               -0.043472697                0.130623922 
##             YOB.Age.fctr.Q             YOB.Age.fctr^4 
##                0.036878348                0.054188683 
##             YOB.Age.fctr^6             YOB.Age.fctr^7 
##                0.013010153               -0.049328694 
##             YOB.Age.fctr^8 
##               -0.071002509
## [1] TRUE
# From here to save(), this should all be in one function
#   these are executed in the same seq twice more:
#       fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId, 
                                 rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId, 
                                     rsp_var = glb_rsp_var)

print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))
##                            All.X..rcv.glmnet.imp         imp
## Q109244.fctrYes                      100.0000000 100.0000000
## Hhold.fctrPKn                         67.4470865  67.4470865
## Q109244.fctrNo                        48.9989248  48.9989248
## Q115611.fctrYes                       42.9430763  42.9430763
## Q98869.fctrNo                         34.8166016  34.8166016
## Q98197.fctrNo                         24.1226162  24.1226162
## Q116881.fctrRight                     18.5423722  18.5423722
## Hhold.fctrMKy                         18.2930135  18.2930135
## Q99480.fctrNo                         17.6167561  17.6167561
## Income.fctr.C                         16.9676954  16.9676954
## YOB.Age.fctr.L                        16.7831959  16.7831959
## Q115611.fctrNo                        15.9346193  15.9346193
## Q118232.fctrId                        15.6916063  15.6916063
## Q120379.fctrYes                       15.4104054  15.4104054
## Q122771.fctrPt                        15.0245450  15.0245450
## Q119851.fctrNo                        14.8555069  14.8555069
## Edn.fctr^4                            14.6009991  14.6009991
## Q101163.fctrDad                       14.5919797  14.5919797
## Hhold.fctrSKy                         14.4618408  14.4618408
## Q113181.fctrYes                       13.3432856  13.3432856
## Q111220.fctrYes                       13.2553044  13.2553044
## Q100689.fctrYes                       12.5130132  12.5130132
## Q110740.fctrPC                        12.4370866  12.4370866
## Q113181.fctrNo                        12.3774207  12.3774207
## Q106997.fctrYy                        11.3399199  11.3399199
## Income.fctr.Q                         11.2640070  11.2640070
## Q115390.fctrNo                        11.0539640  11.0539640
## Q98197.fctrYes                        10.9851323  10.9851323
## Q115899.fctrCs                        10.7137252  10.7137252
## Gender.fctrM                          10.4380830  10.4380830
## Q116881.fctrHappy                     10.2114793  10.2114793
## Q101163.fctrMom                       10.1075342  10.1075342
## Edn.fctr^7                             9.9362540   9.9362540
## Q98059.fctrYes                         9.4323555   9.4323555
## Q112478.fctrNo                         9.2497254   9.2497254
## YOB.Age.fctr^8                         9.1280457   9.1280457
## Q106389.fctrNo                         9.0336598   9.0336598
## Q108342.fctrOnline                     8.9094877   8.9094877
## Q120194.fctrStudy first                8.4317251   8.4317251
## Q116953.fctrYes                        8.1291402   8.1291402
## YOB.Age.fctr^4                         6.8811418   6.8811418
## Q121699.fctrNo                         6.8528131   6.8528131
## Q120379.fctrNo                         6.5771042   6.5771042
## YOB.Age.fctr^7                         6.2723892   6.2723892
## Q108855.fctrYes!                       6.2014763   6.2014763
## Q104996.fctrNo                         6.1051871   6.1051871
## Q106997.fctrGr                         5.9687582   5.9687582
## Q105655.fctrYes                        5.8638558   5.8638558
## Q121699.fctrYes                        5.6842085   5.6842085
## Q99480.fctrYes                         5.5733945   5.5733945
## Q98578.fctrNo                          5.5704173   5.5704173
## Q108950.fctrRisk-friendly              5.4354801   5.4354801
## Q106272.fctrYes                        5.3991578   5.3991578
## Q120012.fctrYes                        5.2631178   5.2631178
## Edn.fctr^6                             5.0863780   5.0863780
## Q124742.fctrNo                         4.9268126   4.9268126
## Q120472.fctrScience                    4.9152606   4.9152606
## Q106042.fctrNo                         4.8336001   4.8336001
## Q116953.fctrNo                         4.7674175   4.7674175
## Q102687.fctrYes                        4.6546081   4.6546081
## YOB.Age.fctr.Q                         4.5076490   4.5076490
## Q124122.fctrNo                         4.4792090   4.4792090
## Q120014.fctrYes                        4.2069796   4.2069796
## Q120014.fctrNo                         4.1218869   4.1218869
## Q116197.fctrA.M.                       4.0683720   4.0683720
## Q120650.fctrYes                        3.8615056   3.8615056
## Q111848.fctrYes                        3.7346743   3.7346743
## Q115390.fctrYes                        3.2168145   3.2168145
## Q122120.fctrYes                        3.1386809   3.1386809
## Q123464.fctrNo                         3.0681329   3.0681329
## Q96024.fctrNo                          2.9856110   2.9856110
## Hhold.fctrSKn                          2.9323698   2.9323698
## Q107491.fctrYes                        2.8048927   2.8048927
## Income.fctr^4                          2.7754752   2.7754752
## Q118233.fctrNo                         2.6918042   2.6918042
## Q117186.fctrHot headed                 2.4574162   2.4574162
## Q113992.fctrYes                        2.3052153   2.3052153
## Q121700.fctrYes                        2.2123810   2.2123810
## Q110740.fctrMac                        2.1456057   2.1456057
## Q100010.fctrNo                         1.8898404   1.8898404
## Q118233.fctrYes                        1.8137700   1.8137700
## Q119851.fctrYes                        1.7347117   1.7347117
## Q104996.fctrYes                        1.6878881   1.6878881
## Q115899.fctrMe                         1.6218096   1.6218096
## YOB.Age.fctr^6                         1.5149593   1.5149593
## Q121700.fctrNo                         1.3562224   1.3562224
## Q101596.fctrNo                         1.2631861   1.2631861
## Q101162.fctrPessimist                  1.1829972   1.1829972
## Q98059.fctrOnly-child                  1.0772910   1.0772910
## Q100680.fctrYes                        0.6778259   0.6778259
## Q119650.fctrGiving                     0.6702535   0.6702535
## Q115602.fctrNo                         0.6495190   0.6495190
## Q114386.fctrMysterious                 0.5881439   0.5881439
## Q105840.fctrNo                         0.5659680   0.5659680
## Income.fctr^6                          0.5493890   0.5493890
## Q124122.fctrYes                        0.5319589   0.5319589
## Q103293.fctrNo                         0.4231161   0.4231161
## Q117193.fctrStandard hours             0.3158898   0.3158898
## Q112270.fctrYes                        0.2683233   0.2683233
## Q114152.fctrYes                        0.1288345   0.1288345
## .rnorm                                 0.0000000   0.0000000
## Edn.fctr.L                             0.0000000   0.0000000
## Edn.fctr.Q                             0.0000000   0.0000000
## Edn.fctr.C                             0.0000000   0.0000000
## Edn.fctr^5                             0.0000000   0.0000000
## Gender.fctrF                           0.0000000   0.0000000
## Hhold.fctrMKn                          0.0000000   0.0000000
## Hhold.fctrPKy                          0.0000000   0.0000000
## Income.fctr.L                          0.0000000   0.0000000
## Income.fctr^5                          0.0000000   0.0000000
## Q100010.fctrYes                        0.0000000   0.0000000
## Q100562.fctrNo                         0.0000000   0.0000000
## Q100562.fctrYes                        0.0000000   0.0000000
## Q100680.fctrNo                         0.0000000   0.0000000
## Q100689.fctrNo                         0.0000000   0.0000000
## Q101162.fctrOptimist                   0.0000000   0.0000000
## Q101596.fctrYes                        0.0000000   0.0000000
## Q102089.fctrOwn                        0.0000000   0.0000000
## Q102089.fctrRent                       0.0000000   0.0000000
## Q102289.fctrNo                         0.0000000   0.0000000
## Q102289.fctrYes                        0.0000000   0.0000000
## Q102674.fctrNo                         0.0000000   0.0000000
## Q102674.fctrYes                        0.0000000   0.0000000
## Q102687.fctrNo                         0.0000000   0.0000000
## Q102906.fctrNo                         0.0000000   0.0000000
## Q102906.fctrYes                        0.0000000   0.0000000
## Q103293.fctrYes                        0.0000000   0.0000000
## Q105655.fctrNo                         0.0000000   0.0000000
## Q105840.fctrYes                        0.0000000   0.0000000
## Q106042.fctrYes                        0.0000000   0.0000000
## Q106272.fctrNo                         0.0000000   0.0000000
## Q106388.fctrNo                         0.0000000   0.0000000
## Q106388.fctrYes                        0.0000000   0.0000000
## Q106389.fctrYes                        0.0000000   0.0000000
## Q106993.fctrNo                         0.0000000   0.0000000
## Q106993.fctrYes                        0.0000000   0.0000000
## Q107491.fctrNo                         0.0000000   0.0000000
## Q107869.fctrNo                         0.0000000   0.0000000
## Q107869.fctrYes                        0.0000000   0.0000000
## Q108342.fctrIn-person                  0.0000000   0.0000000
## Q108343.fctrNo                         0.0000000   0.0000000
## Q108343.fctrYes                        0.0000000   0.0000000
## Q108617.fctrNo                         0.0000000   0.0000000
## Q108617.fctrYes                        0.0000000   0.0000000
## Q108754.fctrNo                         0.0000000   0.0000000
## Q108754.fctrYes                        0.0000000   0.0000000
## Q108855.fctrUmm...                     0.0000000   0.0000000
## Q108856.fctrSocialize                  0.0000000   0.0000000
## Q108856.fctrSpace                      0.0000000   0.0000000
## Q108950.fctrCautious                   0.0000000   0.0000000
## Q109367.fctrNo                         0.0000000   0.0000000
## Q109367.fctrYes                        0.0000000   0.0000000
## Q111220.fctrNo                         0.0000000   0.0000000
## Q111580.fctrDemanding                  0.0000000   0.0000000
## Q111580.fctrSupportive                 0.0000000   0.0000000
## Q111848.fctrNo                         0.0000000   0.0000000
## Q112270.fctrNo                         0.0000000   0.0000000
## Q112478.fctrYes                        0.0000000   0.0000000
## Q112512.fctrNo                         0.0000000   0.0000000
## Q112512.fctrYes                        0.0000000   0.0000000
## Q113583.fctrTalk                       0.0000000   0.0000000
## Q113583.fctrTunes                      0.0000000   0.0000000
## Q113584.fctrPeople                     0.0000000   0.0000000
## Q113584.fctrTechnology                 0.0000000   0.0000000
## Q113992.fctrNo                         0.0000000   0.0000000
## Q114152.fctrNo                         0.0000000   0.0000000
## Q114386.fctrTMI                        0.0000000   0.0000000
## Q114517.fctrNo                         0.0000000   0.0000000
## Q114517.fctrYes                        0.0000000   0.0000000
## Q114748.fctrNo                         0.0000000   0.0000000
## Q114748.fctrYes                        0.0000000   0.0000000
## Q114961.fctrNo                         0.0000000   0.0000000
## Q114961.fctrYes                        0.0000000   0.0000000
## Q115195.fctrNo                         0.0000000   0.0000000
## Q115195.fctrYes                        0.0000000   0.0000000
## Q115602.fctrYes                        0.0000000   0.0000000
## Q115610.fctrNo                         0.0000000   0.0000000
## Q115610.fctrYes                        0.0000000   0.0000000
## Q115777.fctrEnd                        0.0000000   0.0000000
## Q115777.fctrStart                      0.0000000   0.0000000
## Q116197.fctrP.M.                       0.0000000   0.0000000
## Q116441.fctrNo                         0.0000000   0.0000000
## Q116441.fctrYes                        0.0000000   0.0000000
## Q116448.fctrNo                         0.0000000   0.0000000
## Q116448.fctrYes                        0.0000000   0.0000000
## Q116601.fctrNo                         0.0000000   0.0000000
## Q116601.fctrYes                        0.0000000   0.0000000
## Q116797.fctrNo                         0.0000000   0.0000000
## Q116797.fctrYes                        0.0000000   0.0000000
## Q117186.fctrCool headed                0.0000000   0.0000000
## Q117193.fctrOdd hours                  0.0000000   0.0000000
## Q118117.fctrNo                         0.0000000   0.0000000
## Q118117.fctrYes                        0.0000000   0.0000000
## Q118232.fctrPr                         0.0000000   0.0000000
## Q118237.fctrNo                         0.0000000   0.0000000
## Q118237.fctrYes                        0.0000000   0.0000000
## Q118892.fctrNo                         0.0000000   0.0000000
## Q118892.fctrYes                        0.0000000   0.0000000
## Q119334.fctrNo                         0.0000000   0.0000000
## Q119334.fctrYes                        0.0000000   0.0000000
## Q119650.fctrReceiving                  0.0000000   0.0000000
## Q120012.fctrNo                         0.0000000   0.0000000
## Q120194.fctrTry first                  0.0000000   0.0000000
## Q120472.fctrArt                        0.0000000   0.0000000
## Q120650.fctrNo                         0.0000000   0.0000000
## Q120978.fctrNo                         0.0000000   0.0000000
## Q120978.fctrYes                        0.0000000   0.0000000
## Q121011.fctrNo                         0.0000000   0.0000000
## Q121011.fctrYes                        0.0000000   0.0000000
## Q122120.fctrNo                         0.0000000   0.0000000
## Q122769.fctrNo                         0.0000000   0.0000000
## Q122769.fctrYes                        0.0000000   0.0000000
## Q122770.fctrNo                         0.0000000   0.0000000
## Q122770.fctrYes                        0.0000000   0.0000000
## Q122771.fctrPc                         0.0000000   0.0000000
## Q123464.fctrYes                        0.0000000   0.0000000
## Q123621.fctrNo                         0.0000000   0.0000000
## Q123621.fctrYes                        0.0000000   0.0000000
## Q124742.fctrYes                        0.0000000   0.0000000
## Q96024.fctrYes                         0.0000000   0.0000000
## Q98078.fctrNo                          0.0000000   0.0000000
## Q98078.fctrYes                         0.0000000   0.0000000
## Q98578.fctrYes                         0.0000000   0.0000000
## Q98869.fctrYes                         0.0000000   0.0000000
## Q99581.fctrNo                          0.0000000   0.0000000
## Q99581.fctrYes                         0.0000000   0.0000000
## Q99716.fctrNo                          0.0000000   0.0000000
## Q99716.fctrYes                         0.0000000   0.0000000
## Q99982.fctrCheck!                      0.0000000   0.0000000
## Q99982.fctrNope                        0.0000000   0.0000000
## YOB.Age.fctr.C                         0.0000000   0.0000000
## YOB.Age.fctr^5                         0.0000000   0.0000000
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))

# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
    if (!is.null(featsimp_df <- glb_featsimp_df)) {
        featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))    
        featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
        featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)    
        featsimp_df$feat.interact <- 
            ifelse(featsimp_df$feat.interact == featsimp_df$feat, 
                                            NA, featsimp_df$feat.interact)
        featsimp_df$feat <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
        featsimp_df$feat.interact <- 
            gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact) 
        featsimp_df <- orderBy(~ -imp.max, 
            summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
                      FUN=max))    
        #rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])    
        
        featsimp_df <- subset(featsimp_df, !is.na(imp.max))
        if (nrow(featsimp_df) > 5) {
            warning("Limiting important feature scatter plots to 5 out of ",
                    nrow(featsimp_df))
            featsimp_df <- head(featsimp_df, 5)
        }
        
    #     if (!all(is.na(featsimp_df$feat.interact)))
    #         stop("not implemented yet")
        rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
        for (var in featsimp_df$feat) {
            plot_df <- melt(obs_df, id.vars = var, 
                            measure.vars = c(glb_rsp_var, rsp_var_out))
    
            print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
                                facet_colcol_name = "variable", jitter = TRUE) + 
                          guides(color = FALSE))
        }
    }
    
    if (glb_is_regression) {
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No important features in glb_fin_mdl") else
            print(myplot_prediction_regression(df=obs_df, 
                        feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
                                      ".rownames"), 
                                               feat_y=featsimp_df$feat[1],
                        rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
                        id_vars=glbFeatsId)
    #               + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
    #               + geom_point(aes_string(color="<col_name>.fctr")) #  to color the plot
                  )
    }    
    
    if (glb_is_classification) {
        require(lazyeval)
        
        if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
            warning("No features in selected model are statistically important")
        else print(myplot_prediction_classification(df = obs_df, 
                                feat_x = ifelse(nrow(featsimp_df) > 1, 
                                                featsimp_df$feat[2], ".rownames"),
                                               feat_y = featsimp_df$feat[1],
                                                rsp_var = glb_rsp_var, 
                                                rsp_var_out = rsp_var_out, 
                                                id_vars = glbFeatsId,
                                                prob_threshold = prob_threshold))
    }    
}

if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId, 
            prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId, 
                                           "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id =
## glbMdlSelId, : Limiting important feature scatter plots to 5 out of 107

## Loading required package: lazyeval

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    1393          D                         0.2330384
## 2    2798          D                         0.2333508
## 3    4075          D                         0.2351937
## 4    1843          D                         0.2499408
## 5    1187          D                         0.2510454
## 6    1045          D                         0.2563875
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            R                             TRUE
## 2                            R                             TRUE
## 3                            R                             TRUE
## 4                            R                             TRUE
## 5                            R                             TRUE
## 6                            R                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.7669616                               FALSE
## 2                            0.7666492                               FALSE
## 3                            0.7648063                               FALSE
## 4                            0.7500592                               FALSE
## 5                            0.7489546                               FALSE
## 6                            0.7436125                               FALSE
##   Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1                                 FALSE                         -0.4169616
## 2                                 FALSE                         -0.4166492
## 3                                 FALSE                         -0.4148063
## 4                                 FALSE                         -0.4000592
## 5                                 FALSE                         -0.3989546
## 6                                 FALSE                         -0.3936125
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 12     1230          D                         0.2839466
## 99     3957          D                         0.4465035
## 193    4171          D                         0.5180425
## 235    4002          D                         0.5375024
## 288    5416          D                         0.5542771
## 421    6343          R                         0.6540221
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 12                             R                             TRUE
## 99                             R                             TRUE
## 193                            R                             TRUE
## 235                            R                             TRUE
## 288                            R                             TRUE
## 421                            D                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 12                             0.7160534
## 99                             0.5534965
## 193                            0.4819575
## 235                            0.4624976
## 288                            0.4457229
## 421                            0.6540221
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 12                                FALSE
## 99                                FALSE
## 193                               FALSE
## 235                               FALSE
## 288                               FALSE
## 421                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 12                                  FALSE
## 99                                  FALSE
## 193                                 FALSE
## 235                                 FALSE
## 288                                 FALSE
## 421                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 12                         -0.36605344
## 99                         -0.20349647
## 193                        -0.13195753
## 235                        -0.11249756
## 288                        -0.09572293
## 421                         0.00402211
##     USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 464    5148          R                         0.8307342
## 465    1118          R                         0.8371768
## 466    4010          R                         0.8416294
## 467    3921          R                         0.8597362
## 468    1307          R                         0.8832304
## 469     451          R                         0.8853558
##     Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 464                            D                             TRUE
## 465                            D                             TRUE
## 466                            D                             TRUE
## 467                            D                             TRUE
## 468                            D                             TRUE
## 469                            D                             TRUE
##     Party.fctr.All.X..rcv.glmnet.err.abs
## 464                            0.8307342
## 465                            0.8371768
## 466                            0.8416294
## 467                            0.8597362
## 468                            0.8832304
## 469                            0.8853558
##     Party.fctr.All.X..rcv.glmnet.is.acc
## 464                               FALSE
## 465                               FALSE
## 466                               FALSE
## 467                               FALSE
## 468                               FALSE
## 469                               FALSE
##     Party.fctr.All.X..rcv.glmnet.accurate
## 464                                 FALSE
## 465                                 FALSE
## 466                                 FALSE
## 467                                 FALSE
## 468                                 FALSE
## 469                                 FALSE
##     Party.fctr.All.X..rcv.glmnet.error
## 464                          0.1807342
## 465                          0.1871768
## 466                          0.1916294
## 467                          0.2097362
## 468                          0.2332304
## 469                          0.2353558

if (!is.null(glbFeatsCategory)) {
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId, 
                                 label = "fit"), 
                            by = glbFeatsCategory, all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    glbLvlCategory <- merge(glbLvlCategory, 
            myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
                                 label="OOB"),
                          #by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
                          all = TRUE)
    row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
    if (any(grepl("OOB", glbMdlMetricsEval)))
        print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
            print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
    print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}
##     Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## PKy        PKy      9     52     10     0.01169065    0.008035714
## PKn        PKn     30    150     37     0.03372302    0.026785714
## N            N     83    367    102     0.08250899    0.074107143
## SKn        SKn    511   1920    638     0.43165468    0.456250000
## MKn        MKn    136    516    169     0.11600719    0.121428571
## SKy        SKy     53    147     65     0.03304856    0.047321429
## MKy        MKy    298   1296    371     0.29136691    0.266071429
##     .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## PKy    0.007183908        24.55182        0.4721503     52         4.44135
## PKn    0.026580460        54.28673        0.3619116    150        14.22010
## N      0.073275862       169.17762        0.4609745    367        37.99992
## SKn    0.458333333       855.61896        0.4456349   1920       233.08138
## MKn    0.121408046       227.85830        0.4415859    516        61.65902
## SKy    0.046695402        61.84682        0.4207267    147        23.64664
## MKy    0.266522989       572.19199        0.4415062   1296       130.77361
##     err.abs.OOB.mean
## PKy        0.4934833
## PKn        0.4740034
## N          0.4578304
## SKn        0.4561280
## MKn        0.4533752
## SKy        0.4461630
## MKy        0.4388376
##           .n.OOB           .n.Fit           .n.Tst   .freqRatio.Fit 
##      1120.000000      4448.000000      1392.000000         1.000000 
##   .freqRatio.OOB   .freqRatio.Tst  err.abs.fit.sum err.abs.fit.mean 
##         1.000000         1.000000      1965.532248         3.044490 
##           .n.fit  err.abs.OOB.sum err.abs.OOB.mean 
##      4448.000000       505.822031         3.219821
write.csv(glbObsOOB[, c(glbFeatsId, 
                grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))], 
    paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE), 
           "_OOBobs.csv"), row.names=FALSE)

fit.models_2_chunk_df <- 
    myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")
##              label step_major step_minor label_minor     bgn end elapsed
## 1 fit.models_2_bgn          1          0    teardown 277.348  NA      NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
##        label step_major step_minor label_minor     bgn     end elapsed
## 6 fit.models          4          2           2 264.676 277.359  12.683
## 7 fit.models          4          3           3 277.359      NA      NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
#         stop("fit.models_3: Why is this happening ?")

#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
    # Merge or cbind ?
    for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
        glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
    for (col in setdiff(names(glbObsFit), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
    if (all(is.na(glbObsNew[, glb_rsp_var])))
        for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
            glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
    for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
        glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "model.selected")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0

glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
##               label step_major step_minor label_minor     bgn     end
## 7        fit.models          4          3           3 277.359 281.996
## 8 fit.data.training          5          0           0 281.996      NA
##   elapsed
## 7   4.637
## 8      NA

Step 5.0: fit data training

#load(paste0(glb_inp_pfx, "dsk.RData"))

if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
    warning("Final model same as user selected model")
    glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else 
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{    
    warning("Final model same as glbMdlSelId")
    glbMdlFinId <- paste0("Final.", glbMdlSelId)
    glb_fin_mdl <- glb_sel_mdl
    glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
    mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
    mdlDf$id <- glbMdlFinId
    glb_models_df <- rbind(glb_models_df, mdlDf)
} else {    
            if (grepl("RFE\\.X", names(glbMdlFamilies))) {
                indepVar <- mygetIndepVar(glb_feats_df)
                rfe_trn_results <- 
                    myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
                if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
                                      sort(predictors(rfe_fit_results))))) {
                    print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
                    print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
                    print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
                    print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
            }
        }
    # }    

    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        # Fit selected models on glbObsTrn
        for (mdl_id in gsub(".prob", "", 
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
                            fixed = TRUE)) {
            mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
            mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"), 
                               collapse = ".")
            if (grepl("RFE\\.X\\.", mdlIdPfx)) 
                mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
                    predictors(rfe_trn_results))) else
                mdlIndepVars <- trim(unlist(
            strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
            ret_lst <- 
                myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                        id.prefix = mdlIdPfx, 
                        type = glb_model_type, tune.df = glbMdlTuneParams,
                        trainControl.method = "repeatedcv",
                        trainControl.number = glb_rcv_n_folds,
                        trainControl.repeats = glb_rcv_n_repeats,
                        trainControl.classProbs = glb_is_classification,
                        trainControl.summaryFunction = glbMdlMetricSummaryFn,
                        train.metric = glbMdlMetricSummary, 
                        train.maximize = glbMdlMetricMaximize,    
                        train.method = tail(mdl_id_components, 1))),
                    indepVar = mdlIndepVars,
                    rsp_var = glb_rsp_var, 
                    fit_df = glbObsTrn, OOB_df = NULL)
            
            glbObsTrn <- glb_get_predictions(df = glbObsTrn,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
            glbObsNew <- glb_get_predictions(df = glbObsNew,
                                                mdl_id = tail(glb_models_df$id, 1), 
                                                rsp_var = glb_rsp_var,
                                                prob_threshold_def = 
                    subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
        }    
    }
    
    # "Final" model
    if ((model_method <- glb_sel_mdl$method) == "custom")
        # get actual method from the mdl_id
        model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
        
    if (grepl("Ensemble", glbMdlSelId)) {
        # Find which models are relevant
        mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
        if (glb_is_classification && glb_is_binomial)
            indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
                                    row.names(mdlimp_df)) else
            indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
                                    row.names(mdlimp_df))
    } else 
    if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
        indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
    } else indepVar <- 
                trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
                                                   glbMdlSelId
                                                   , "feats"], "[,]")))
        
    if (!is.null(glb_preproc_methods) &&
        ((match_pos <- regexpr(gsub(".", "\\.", 
                                    paste(glb_preproc_methods, collapse = "|"),
                                   fixed = TRUE), glbMdlSelId)) != -1))
        ths_preProcess <- str_sub(glbMdlSelId, match_pos, 
                                match_pos + attr(match_pos, "match.length") - 1) else
        ths_preProcess <- NULL                                      

    mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
                                   "Final.Ensemble", "Final")
    
    trnobs_df <- glbObsTrn 
    if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
        trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
        print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
        print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
    }    
        
    # Force fitting of Final.glm to identify outliers
    method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
    for (method in method_vctr) {
        #source("caret_nominalTrainWorkflow.R")
        
        # glmnet requires at least 2 indep vars
        if ((length(indepVar) == 1) && (method %in% "glmnet"))
            next
        
        ret_lst <- 
            myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
                    id.prefix = mdl_id_pfx, 
                    type = glb_model_type, trainControl.method = "repeatedcv",
                    trainControl.number = glb_rcv_n_folds, 
                    trainControl.repeats = glb_rcv_n_repeats,
                    trainControl.classProbs = glb_is_classification,
                    trainControl.summaryFunction = glbMdlMetricSummaryFn,
                    trainControl.allowParallel = glbMdlAllowParallel,
                    train.metric = glbMdlMetricSummary, 
                    train.maximize = glbMdlMetricMaximize,    
                    train.method = method,
                    train.preProcess = ths_preProcess)),
                indepVar = indepVar, rsp_var = glb_rsp_var, 
                fit_df = trnobs_df, OOB_df = NULL)
        
        if ((length(method_vctr) == 1) || (method != "glm")) {
            glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]] 
            glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
        }
    }
        
}
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] "    indepVar: Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr"
## [1] "myfit_mdl: setup complete: 0.692000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0113 on full training set
## [1] "myfit_mdl: train complete: 28.033000 secs"

##             Length Class      Mode     
## a0             77  -none-     numeric  
## beta        17864  dgCMatrix  S4       
## df             77  -none-     numeric  
## dim             2  -none-     numeric  
## lambda         77  -none-     numeric  
## dev.ratio      77  -none-     numeric  
## nulldev         1  -none-     numeric  
## npasses         1  -none-     numeric  
## jerr            1  -none-     numeric  
## offset          1  -none-     logical  
## classnames      2  -none-     character
## call            5  -none-     call     
## nobs            1  -none-     numeric  
## lambdaOpt       1  -none-     numeric  
## xNames        232  -none-     character
## problemType     1  -none-     character
## tuneValue       2  data.frame list     
## obsLevels       2  -none-     character
## [1] "min lambda > lambdaOpt:"
##             (Intercept)              Edn.fctr.L            Gender.fctrM 
##            2.123071e-01            2.157532e-02           -1.089012e-01 
##           Hhold.fctrMKy           Hhold.fctrPKn           Income.fctr.Q 
##           -8.164986e-02            3.776989e-01           -5.982145e-02 
##           Income.fctr.C         Q100689.fctrYes         Q101163.fctrDad 
##           -6.845478e-02            7.776009e-02           -9.435015e-02 
##         Q101163.fctrMom          Q104996.fctrNo          Q106042.fctrNo 
##            5.478042e-02           -2.122253e-02           -2.683896e-02 
##          Q106389.fctrNo          Q106997.fctrGr        Q108855.fctrYes! 
##           -2.242088e-02           -7.229662e-02           -2.697942e-02 
##          Q109244.fctrNo         Q109244.fctrYes         Q110740.fctrMac 
##           -3.570162e-01            9.252278e-01            2.173880e-02 
##          Q110740.fctrPC          Q112478.fctrNo          Q113181.fctrNo 
##           -7.110575e-02           -3.234141e-02            1.047847e-01 
##         Q113181.fctrYes         Q115195.fctrYes          Q115390.fctrNo 
##           -1.442422e-01            1.409950e-02           -6.846845e-03 
##         Q115390.fctrYes          Q115611.fctrNo         Q115611.fctrYes 
##            5.491365e-02            1.353109e-01           -3.314197e-01 
##          Q115899.fctrCs       Q116881.fctrHappy       Q116881.fctrRight 
##            4.819157e-02            1.619212e-02           -1.668260e-01 
##          Q116953.fctrNo          Q118232.fctrId          Q118233.fctrNo 
##           -3.153005e-02            9.756745e-02           -3.955244e-03 
##          Q119851.fctrNo Q120194.fctrStudy first          Q120379.fctrNo 
##           -1.006952e-01            4.033322e-02           -1.266726e-02 
##         Q120379.fctrYes     Q120472.fctrScience         Q120650.fctrYes 
##            8.628830e-02           -8.052739e-02           -7.918516e-05 
##         Q121699.fctrYes         Q122120.fctrYes          Q122771.fctrPt 
##            3.237186e-02           -1.457223e-02           -7.566506e-02 
##          Q124742.fctrNo           Q98197.fctrNo          Q98197.fctrYes 
##            8.531459e-03            2.842728e-01           -3.409862e-03 
##           Q98869.fctrNo           Q99480.fctrNo          Q99480.fctrYes 
##            1.875869e-01            3.711351e-02           -4.728446e-02 
##          YOB.Age.fctr.L          YOB.Age.fctr^7          YOB.Age.fctr^8 
##            6.134774e-02           -1.275503e-02           -3.263560e-02 
## [1] "max lambda < lambdaOpt:"
##             (Intercept)              Edn.fctr.L            Gender.fctrM 
##            0.2194026415            0.0250789647           -0.1110166523 
##           Hhold.fctrMKy           Hhold.fctrPKn           Income.fctr.Q 
##           -0.0922968501            0.3888002722           -0.0675789374 
##           Income.fctr.C         Q100689.fctrYes         Q101163.fctrDad 
##           -0.0815213744            0.0873664932           -0.0968737805 
##         Q101163.fctrMom          Q104996.fctrNo          Q106042.fctrNo 
##            0.0605524619           -0.0294158307           -0.0293613758 
##          Q106389.fctrNo          Q106997.fctrGr        Q108855.fctrYes! 
##           -0.0310950390           -0.0813603310           -0.0347190523 
##          Q109244.fctrNo         Q109244.fctrYes         Q110740.fctrMac 
##           -0.3582630105            0.9302958326            0.0289083519 
##          Q110740.fctrPC         Q111220.fctrYes          Q112478.fctrNo 
##           -0.0732343770            0.0047213847           -0.0404980916 
##          Q113181.fctrNo         Q113181.fctrYes       Q113583.fctrTunes 
##            0.1096475233           -0.1426961792            0.0001044441 
##         Q115195.fctrYes          Q115390.fctrNo         Q115390.fctrYes 
##            0.0225819717           -0.0163258587            0.0569504027 
##          Q115611.fctrNo         Q115611.fctrYes          Q115899.fctrCs 
##            0.1359076104           -0.3370943566            0.0566104909 
##       Q116881.fctrHappy       Q116881.fctrRight          Q116953.fctrNo 
##            0.0256811888           -0.1671335987           -0.0421907026 
##          Q118232.fctrId          Q118233.fctrNo          Q119851.fctrNo 
##            0.1082336575           -0.0147693511           -0.1069967848 
## Q120194.fctrStudy first          Q120379.fctrNo         Q120379.fctrYes 
##            0.0499363227           -0.0112236709            0.0989672666 
##     Q120472.fctrScience         Q120650.fctrYes         Q121699.fctrYes 
##           -0.0870280043           -0.0114635573            0.0410727129 
##         Q122120.fctrYes          Q122771.fctrPt          Q124742.fctrNo 
##           -0.0240492262           -0.0868530842            0.0215413569 
##           Q98197.fctrNo           Q98869.fctrNo           Q99480.fctrNo 
##            0.2931645340            0.1970976751            0.0382915209 
##          Q99480.fctrYes          YOB.Age.fctr.L          YOB.Age.fctr^7 
##           -0.0578658541            0.0762294190           -0.0256109530 
##          YOB.Age.fctr^8 
##           -0.0451630163 
## [1] "myfit_mdl: train diagnostics complete: 28.698000 secs"

##          Prediction
## Reference    R    D
##         R 2323  294
##         D 1902 1049
##       Accuracy          Kappa  AccuracyLower  AccuracyUpper   AccuracyNull 
##   6.056034e-01   2.349603e-01   5.926242e-01   6.184720e-01   5.299928e-01 
## AccuracyPValue  McnemarPValue 
##   3.833186e-30  1.013594e-257 
## [1] "myfit_mdl: predict complete: 36.135000 secs"
##                  id
## 1 Final##rcv#glmnet
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    feats
## 1 Q109244.fctr,Hhold.fctr,Edn.fctr,Q101163.fctr,Q100689.fctr,Q98078.fctr,Q99716.fctr,Q120379.fctr,Q121699.fctr,Q105840.fctr,Q113583.fctr,Q115195.fctr,Q102089.fctr,Q98059.fctr,Q114386.fctr,Q100680.fctr,Q108342.fctr,Q111848.fctr,YOB.Age.fctr,Q118892.fctr,Q102687.fctr,Q115390.fctr,Q119851.fctr,Q114517.fctr,Q120012.fctr,Q109367.fctr,Q114961.fctr,Q121700.fctr,Q124122.fctr,Q111220.fctr,Q113992.fctr,Q121011.fctr,Q106042.fctr,Q116448.fctr,Q116601.fctr,Q104996.fctr,Q102906.fctr,Q113584.fctr,Q108950.fctr,Q102674.fctr,Q103293.fctr,Q112478.fctr,Q114748.fctr,Q107491.fctr,Q100562.fctr,Q108617.fctr,Q100010.fctr,Q115602.fctr,Q116953.fctr,Q115610.fctr,Q106997.fctr,Q120978.fctr,Q112512.fctr,Q108343.fctr,Q96024.fctr,Q106389.fctr,.rnorm,Q108754.fctr,Q98578.fctr,Q101162.fctr,Q115777.fctr,Q99581.fctr,Q124742.fctr,Q116797.fctr,Q112270.fctr,Q118237.fctr,Q119650.fctr,Q111580.fctr,Q123464.fctr,Q117193.fctr,Q99982.fctr,Q108856.fctr,Q118233.fctr,Q102289.fctr,Q116197.fctr,Income.fctr,Q118232.fctr,Q120194.fctr,Q114152.fctr,Q122770.fctr,Q117186.fctr,Q105655.fctr,Q106993.fctr,Q119334.fctr,Q122120.fctr,Q116441.fctr,Q118117.fctr,Q123621.fctr,Q122769.fctr,Q120650.fctr,Q98869.fctr,Q107869.fctr,Q120014.fctr,Q115899.fctr,Q106388.fctr,Q99480.fctr,Q122771.fctr,Q108855.fctr,Q110740.fctr,Q106272.fctr,Q101596.fctr,Q116881.fctr,Q120472.fctr,Q98197.fctr,Q113181.fctr,Q115611.fctr,Gender.fctr
##   max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1              25                     27.204                 2.304
##   max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1       0.6424822    0.5716469    0.7133175       0.2937147
##   opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1                    0.6       0.6790412        0.6343978
##   max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1             0.5926242              0.618472     0.2626432
##   max.AccuracySD.fit max.KappaSD.fit
## 1         0.01458328      0.02942434
## [1] "myfit_mdl: exit: 36.151000 secs"
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
##               label step_major step_minor label_minor     bgn     end
## 8 fit.data.training          5          0           0 281.996 318.737
## 9 fit.data.training          5          1           1 318.738      NA
##   elapsed
## 8  36.741
## 9      NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial) 
    prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
                                        "opt.prob.threshold.OOB"] else 
    prob_threshold <- NULL

if (grepl("Ensemble", glbMdlFinId)) {
    # Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
    mdlEnsembleComps <- unlist(str_split(subset(glb_models_df, 
                                                id == glbMdlFinId)$feats, ","))
    if (glb_is_classification && glb_is_binomial)
        mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
    mdlEnsembleComps <- gsub(paste0("^", 
                        gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
                             "", mdlEnsembleComps)
    for (mdl_id in mdlEnsembleComps) {
        glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
        glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id, 
                                            rsp_var = glb_rsp_var,
                                            prob_threshold_def = prob_threshold)
    }    
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId, 
                                     rsp_var = glb_rsp_var,
                                    prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.65
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
                                          featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
##                            All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes                      100.0000000          1.000000e+02
## Hhold.fctrPKn                         67.4470865          4.111360e+01
## Q109244.fctrNo                        48.9989248          3.856398e+01
## Q115611.fctrYes                       42.9430763          3.594481e+01
## Q98197.fctrNo                         24.1226162          3.096120e+01
## Q98869.fctrNo                         34.8166016          2.054829e+01
## Q116881.fctrRight                     18.5423722          1.801125e+01
## Q113181.fctrYes                       13.3432856          1.551456e+01
## Q115611.fctrNo                        15.9346193          1.461994e+01
## Gender.fctrM                          10.4380830          1.181920e+01
## Q113181.fctrNo                        12.3774207          1.146362e+01
## Q119851.fctrNo                        14.8555069          1.106875e+01
## Q118232.fctrId                        15.6916063          1.087203e+01
## Q101163.fctrDad                       14.5919797          1.026223e+01
## Q120379.fctrYes                       15.4104054          9.719875e+00
## Hhold.fctrMKy                         18.2930135          9.153825e+00
## Q120472.fctrScience                    4.9152606          8.898966e+00
## Q100689.fctrYes                       12.5130132          8.700537e+00
## Q122771.fctrPt                        15.0245450          8.525487e+00
## Q106997.fctrGr                         5.9687582          8.093498e+00
## Income.fctr.C                         16.9676954          7.808056e+00
## Q110740.fctrPC                        12.4370866          7.741310e+00
## YOB.Age.fctr.L                        16.7831959          7.099717e+00
## Income.fctr.Q                         11.2640070          6.705236e+00
## Q101163.fctrMom                       10.1075342          6.097245e+00
## Q115390.fctrYes                        3.2168145          5.991141e+00
## Q115899.fctrCs                        10.7137252          5.471650e+00
## Q99480.fctrYes                         5.5733945          5.443518e+00
## Q120194.fctrStudy first                8.4317251          4.661891e+00
## Q99480.fctrNo                         17.6167561          4.042722e+00
## YOB.Age.fctr^8                         9.1280457          3.925603e+00
## Q121699.fctrYes                        5.6842085          3.773720e+00
## Q112478.fctrNo                         9.2497254          3.752883e+00
## Q116953.fctrNo                         4.7674175          3.746097e+00
## Q108855.fctrYes!                       6.2014763          3.160846e+00
## Q106042.fctrNo                         4.8336001          2.977412e+00
## Q106389.fctrNo                         9.0336598          2.699101e+00
## Q110740.fctrMac                        2.1456057          2.576971e+00
## Q104996.fctrNo                         6.1051871          2.554284e+00
## Edn.fctr.L                             0.0000000          2.441089e+00
## Q116881.fctrHappy                     10.2114793          2.053272e+00
## Q122120.fctrYes                        3.1386809          1.878088e+00
## Q115195.fctrYes                        0.0000000          1.795001e+00
## YOB.Age.fctr^7                         6.2723892          1.790989e+00
## Q124742.fctrNo                         4.9268126          1.340212e+00
## Q120379.fctrNo                         6.5771042          1.320296e+00
## Q115390.fctrNo                        11.0539640          1.044548e+00
## Q118233.fctrNo                         2.6918042          7.755926e-01
## Q120650.fctrYes                        3.8615056          3.757408e-01
## Q98197.fctrYes                        10.9851323          2.579574e-01
## Q111220.fctrYes                       13.2553044          1.522855e-01
## Q113583.fctrTunes                      0.0000000          3.368785e-03
## .rnorm                                 0.0000000          0.000000e+00
## Edn.fctr.C                             0.0000000          0.000000e+00
## Edn.fctr.Q                             0.0000000          0.000000e+00
## Edn.fctr^4                            14.6009991          0.000000e+00
## Edn.fctr^5                             0.0000000          0.000000e+00
## Edn.fctr^6                             5.0863780          0.000000e+00
## Edn.fctr^7                             9.9362540          0.000000e+00
## Gender.fctrF                           0.0000000          0.000000e+00
## Hhold.fctrMKn                          0.0000000          0.000000e+00
## Hhold.fctrPKy                          0.0000000          0.000000e+00
## Hhold.fctrSKn                          2.9323698          0.000000e+00
## Hhold.fctrSKy                         14.4618408          0.000000e+00
## Income.fctr.L                          0.0000000          0.000000e+00
## Income.fctr^4                          2.7754752          0.000000e+00
## Income.fctr^5                          0.0000000          0.000000e+00
## Income.fctr^6                          0.5493890          0.000000e+00
## Q100010.fctrNo                         1.8898404          0.000000e+00
## Q100010.fctrYes                        0.0000000          0.000000e+00
## Q100562.fctrNo                         0.0000000          0.000000e+00
## Q100562.fctrYes                        0.0000000          0.000000e+00
## Q100680.fctrNo                         0.0000000          0.000000e+00
## Q100680.fctrYes                        0.6778259          0.000000e+00
## Q100689.fctrNo                         0.0000000          0.000000e+00
## Q101162.fctrOptimist                   0.0000000          0.000000e+00
## Q101162.fctrPessimist                  1.1829972          0.000000e+00
## Q101596.fctrNo                         1.2631861          0.000000e+00
## Q101596.fctrYes                        0.0000000          0.000000e+00
## Q102089.fctrOwn                        0.0000000          0.000000e+00
## Q102089.fctrRent                       0.0000000          0.000000e+00
## Q102289.fctrNo                         0.0000000          0.000000e+00
## Q102289.fctrYes                        0.0000000          0.000000e+00
## Q102674.fctrNo                         0.0000000          0.000000e+00
## Q102674.fctrYes                        0.0000000          0.000000e+00
## Q102687.fctrNo                         0.0000000          0.000000e+00
## Q102687.fctrYes                        4.6546081          0.000000e+00
## Q102906.fctrNo                         0.0000000          0.000000e+00
## Q102906.fctrYes                        0.0000000          0.000000e+00
## Q103293.fctrNo                         0.4231161          0.000000e+00
## Q103293.fctrYes                        0.0000000          0.000000e+00
## Q104996.fctrYes                        1.6878881          0.000000e+00
## Q105655.fctrNo                         0.0000000          0.000000e+00
## Q105655.fctrYes                        5.8638558          0.000000e+00
## Q105840.fctrNo                         0.5659680          0.000000e+00
## Q105840.fctrYes                        0.0000000          0.000000e+00
## Q106042.fctrYes                        0.0000000          0.000000e+00
## Q106272.fctrNo                         0.0000000          0.000000e+00
## Q106272.fctrYes                        5.3991578          0.000000e+00
## Q106388.fctrNo                         0.0000000          0.000000e+00
## Q106388.fctrYes                        0.0000000          0.000000e+00
## Q106389.fctrYes                        0.0000000          0.000000e+00
## Q106993.fctrNo                         0.0000000          0.000000e+00
## Q106993.fctrYes                        0.0000000          0.000000e+00
## Q106997.fctrYy                        11.3399199          0.000000e+00
## Q107491.fctrNo                         0.0000000          0.000000e+00
## Q107491.fctrYes                        2.8048927          0.000000e+00
## Q107869.fctrNo                         0.0000000          0.000000e+00
## Q107869.fctrYes                        0.0000000          0.000000e+00
## Q108342.fctrIn-person                  0.0000000          0.000000e+00
## Q108342.fctrOnline                     8.9094877          0.000000e+00
## Q108343.fctrNo                         0.0000000          0.000000e+00
## Q108343.fctrYes                        0.0000000          0.000000e+00
## Q108617.fctrNo                         0.0000000          0.000000e+00
## Q108617.fctrYes                        0.0000000          0.000000e+00
## Q108754.fctrNo                         0.0000000          0.000000e+00
## Q108754.fctrYes                        0.0000000          0.000000e+00
## Q108855.fctrUmm...                     0.0000000          0.000000e+00
## Q108856.fctrSocialize                  0.0000000          0.000000e+00
## Q108856.fctrSpace                      0.0000000          0.000000e+00
## Q108950.fctrCautious                   0.0000000          0.000000e+00
## Q108950.fctrRisk-friendly              5.4354801          0.000000e+00
## Q109367.fctrNo                         0.0000000          0.000000e+00
## Q109367.fctrYes                        0.0000000          0.000000e+00
## Q111220.fctrNo                         0.0000000          0.000000e+00
## Q111580.fctrDemanding                  0.0000000          0.000000e+00
## Q111580.fctrSupportive                 0.0000000          0.000000e+00
## Q111848.fctrNo                         0.0000000          0.000000e+00
## Q111848.fctrYes                        3.7346743          0.000000e+00
## Q112270.fctrNo                         0.0000000          0.000000e+00
## Q112270.fctrYes                        0.2683233          0.000000e+00
## Q112478.fctrYes                        0.0000000          0.000000e+00
## Q112512.fctrNo                         0.0000000          0.000000e+00
## Q112512.fctrYes                        0.0000000          0.000000e+00
## Q113583.fctrTalk                       0.0000000          0.000000e+00
## Q113584.fctrPeople                     0.0000000          0.000000e+00
## Q113584.fctrTechnology                 0.0000000          0.000000e+00
## Q113992.fctrNo                         0.0000000          0.000000e+00
## Q113992.fctrYes                        2.3052153          0.000000e+00
## Q114152.fctrNo                         0.0000000          0.000000e+00
## Q114152.fctrYes                        0.1288345          0.000000e+00
## Q114386.fctrMysterious                 0.5881439          0.000000e+00
## Q114386.fctrTMI                        0.0000000          0.000000e+00
## Q114517.fctrNo                         0.0000000          0.000000e+00
## Q114517.fctrYes                        0.0000000          0.000000e+00
## Q114748.fctrNo                         0.0000000          0.000000e+00
## Q114748.fctrYes                        0.0000000          0.000000e+00
## Q114961.fctrNo                         0.0000000          0.000000e+00
## Q114961.fctrYes                        0.0000000          0.000000e+00
## Q115195.fctrNo                         0.0000000          0.000000e+00
## Q115602.fctrNo                         0.6495190          0.000000e+00
## Q115602.fctrYes                        0.0000000          0.000000e+00
## Q115610.fctrNo                         0.0000000          0.000000e+00
## Q115610.fctrYes                        0.0000000          0.000000e+00
## Q115777.fctrEnd                        0.0000000          0.000000e+00
## Q115777.fctrStart                      0.0000000          0.000000e+00
## Q115899.fctrMe                         1.6218096          0.000000e+00
## Q116197.fctrA.M.                       4.0683720          0.000000e+00
## Q116197.fctrP.M.                       0.0000000          0.000000e+00
## Q116441.fctrNo                         0.0000000          0.000000e+00
## Q116441.fctrYes                        0.0000000          0.000000e+00
## Q116448.fctrNo                         0.0000000          0.000000e+00
## Q116448.fctrYes                        0.0000000          0.000000e+00
## Q116601.fctrNo                         0.0000000          0.000000e+00
## Q116601.fctrYes                        0.0000000          0.000000e+00
## Q116797.fctrNo                         0.0000000          0.000000e+00
## Q116797.fctrYes                        0.0000000          0.000000e+00
## Q116953.fctrYes                        8.1291402          0.000000e+00
## Q117186.fctrCool headed                0.0000000          0.000000e+00
## Q117186.fctrHot headed                 2.4574162          0.000000e+00
## Q117193.fctrOdd hours                  0.0000000          0.000000e+00
## Q117193.fctrStandard hours             0.3158898          0.000000e+00
## Q118117.fctrNo                         0.0000000          0.000000e+00
## Q118117.fctrYes                        0.0000000          0.000000e+00
## Q118232.fctrPr                         0.0000000          0.000000e+00
## Q118233.fctrYes                        1.8137700          0.000000e+00
## Q118237.fctrNo                         0.0000000          0.000000e+00
## Q118237.fctrYes                        0.0000000          0.000000e+00
## Q118892.fctrNo                         0.0000000          0.000000e+00
## Q118892.fctrYes                        0.0000000          0.000000e+00
## Q119334.fctrNo                         0.0000000          0.000000e+00
## Q119334.fctrYes                        0.0000000          0.000000e+00
## Q119650.fctrGiving                     0.6702535          0.000000e+00
## Q119650.fctrReceiving                  0.0000000          0.000000e+00
## Q119851.fctrYes                        1.7347117          0.000000e+00
## Q120012.fctrNo                         0.0000000          0.000000e+00
## Q120012.fctrYes                        5.2631178          0.000000e+00
## Q120014.fctrNo                         4.1218869          0.000000e+00
## Q120014.fctrYes                        4.2069796          0.000000e+00
## Q120194.fctrTry first                  0.0000000          0.000000e+00
## Q120472.fctrArt                        0.0000000          0.000000e+00
## Q120650.fctrNo                         0.0000000          0.000000e+00
## Q120978.fctrNo                         0.0000000          0.000000e+00
## Q120978.fctrYes                        0.0000000          0.000000e+00
## Q121011.fctrNo                         0.0000000          0.000000e+00
## Q121011.fctrYes                        0.0000000          0.000000e+00
## Q121699.fctrNo                         6.8528131          0.000000e+00
## Q121700.fctrNo                         1.3562224          0.000000e+00
## Q121700.fctrYes                        2.2123810          0.000000e+00
## Q122120.fctrNo                         0.0000000          0.000000e+00
## Q122769.fctrNo                         0.0000000          0.000000e+00
## Q122769.fctrYes                        0.0000000          0.000000e+00
## Q122770.fctrNo                         0.0000000          0.000000e+00
## Q122770.fctrYes                        0.0000000          0.000000e+00
## Q122771.fctrPc                         0.0000000          0.000000e+00
## Q123464.fctrNo                         3.0681329          0.000000e+00
## Q123464.fctrYes                        0.0000000          0.000000e+00
## Q123621.fctrNo                         0.0000000          0.000000e+00
## Q123621.fctrYes                        0.0000000          0.000000e+00
## Q124122.fctrNo                         4.4792090          0.000000e+00
## Q124122.fctrYes                        0.5319589          0.000000e+00
## Q124742.fctrYes                        0.0000000          0.000000e+00
## Q96024.fctrNo                          2.9856110          0.000000e+00
## Q96024.fctrYes                         0.0000000          0.000000e+00
## Q98059.fctrOnly-child                  1.0772910          0.000000e+00
## Q98059.fctrYes                         9.4323555          0.000000e+00
## Q98078.fctrNo                          0.0000000          0.000000e+00
## Q98078.fctrYes                         0.0000000          0.000000e+00
## Q98578.fctrNo                          5.5704173          0.000000e+00
## Q98578.fctrYes                         0.0000000          0.000000e+00
## Q98869.fctrYes                         0.0000000          0.000000e+00
## Q99581.fctrNo                          0.0000000          0.000000e+00
## Q99581.fctrYes                         0.0000000          0.000000e+00
## Q99716.fctrNo                          0.0000000          0.000000e+00
## Q99716.fctrYes                         0.0000000          0.000000e+00
## Q99982.fctrCheck!                      0.0000000          0.000000e+00
## Q99982.fctrNope                        0.0000000          0.000000e+00
## YOB.Age.fctr.C                         0.0000000          0.000000e+00
## YOB.Age.fctr.Q                         4.5076490          0.000000e+00
## YOB.Age.fctr^4                         6.8811418          0.000000e+00
## YOB.Age.fctr^5                         0.0000000          0.000000e+00
## YOB.Age.fctr^6                         1.5149593          0.000000e+00
##                                     imp
## Q109244.fctrYes            1.000000e+02
## Hhold.fctrPKn              4.111360e+01
## Q109244.fctrNo             3.856398e+01
## Q115611.fctrYes            3.594481e+01
## Q98197.fctrNo              3.096120e+01
## Q98869.fctrNo              2.054829e+01
## Q116881.fctrRight          1.801125e+01
## Q113181.fctrYes            1.551456e+01
## Q115611.fctrNo             1.461994e+01
## Gender.fctrM               1.181920e+01
## Q113181.fctrNo             1.146362e+01
## Q119851.fctrNo             1.106875e+01
## Q118232.fctrId             1.087203e+01
## Q101163.fctrDad            1.026223e+01
## Q120379.fctrYes            9.719875e+00
## Hhold.fctrMKy              9.153825e+00
## Q120472.fctrScience        8.898966e+00
## Q100689.fctrYes            8.700537e+00
## Q122771.fctrPt             8.525487e+00
## Q106997.fctrGr             8.093498e+00
## Income.fctr.C              7.808056e+00
## Q110740.fctrPC             7.741310e+00
## YOB.Age.fctr.L             7.099717e+00
## Income.fctr.Q              6.705236e+00
## Q101163.fctrMom            6.097245e+00
## Q115390.fctrYes            5.991141e+00
## Q115899.fctrCs             5.471650e+00
## Q99480.fctrYes             5.443518e+00
## Q120194.fctrStudy first    4.661891e+00
## Q99480.fctrNo              4.042722e+00
## YOB.Age.fctr^8             3.925603e+00
## Q121699.fctrYes            3.773720e+00
## Q112478.fctrNo             3.752883e+00
## Q116953.fctrNo             3.746097e+00
## Q108855.fctrYes!           3.160846e+00
## Q106042.fctrNo             2.977412e+00
## Q106389.fctrNo             2.699101e+00
## Q110740.fctrMac            2.576971e+00
## Q104996.fctrNo             2.554284e+00
## Edn.fctr.L                 2.441089e+00
## Q116881.fctrHappy          2.053272e+00
## Q122120.fctrYes            1.878088e+00
## Q115195.fctrYes            1.795001e+00
## YOB.Age.fctr^7             1.790989e+00
## Q124742.fctrNo             1.340212e+00
## Q120379.fctrNo             1.320296e+00
## Q115390.fctrNo             1.044548e+00
## Q118233.fctrNo             7.755926e-01
## Q120650.fctrYes            3.757408e-01
## Q98197.fctrYes             2.579574e-01
## Q111220.fctrYes            1.522855e-01
## Q113583.fctrTunes          3.368785e-03
## .rnorm                     0.000000e+00
## Edn.fctr.C                 0.000000e+00
## Edn.fctr.Q                 0.000000e+00
## Edn.fctr^4                 0.000000e+00
## Edn.fctr^5                 0.000000e+00
## Edn.fctr^6                 0.000000e+00
## Edn.fctr^7                 0.000000e+00
## Gender.fctrF               0.000000e+00
## Hhold.fctrMKn              0.000000e+00
## Hhold.fctrPKy              0.000000e+00
## Hhold.fctrSKn              0.000000e+00
## Hhold.fctrSKy              0.000000e+00
## Income.fctr.L              0.000000e+00
## Income.fctr^4              0.000000e+00
## Income.fctr^5              0.000000e+00
## Income.fctr^6              0.000000e+00
## Q100010.fctrNo             0.000000e+00
## Q100010.fctrYes            0.000000e+00
## Q100562.fctrNo             0.000000e+00
## Q100562.fctrYes            0.000000e+00
## Q100680.fctrNo             0.000000e+00
## Q100680.fctrYes            0.000000e+00
## Q100689.fctrNo             0.000000e+00
## Q101162.fctrOptimist       0.000000e+00
## Q101162.fctrPessimist      0.000000e+00
## Q101596.fctrNo             0.000000e+00
## Q101596.fctrYes            0.000000e+00
## Q102089.fctrOwn            0.000000e+00
## Q102089.fctrRent           0.000000e+00
## Q102289.fctrNo             0.000000e+00
## Q102289.fctrYes            0.000000e+00
## Q102674.fctrNo             0.000000e+00
## Q102674.fctrYes            0.000000e+00
## Q102687.fctrNo             0.000000e+00
## Q102687.fctrYes            0.000000e+00
## Q102906.fctrNo             0.000000e+00
## Q102906.fctrYes            0.000000e+00
## Q103293.fctrNo             0.000000e+00
## Q103293.fctrYes            0.000000e+00
## Q104996.fctrYes            0.000000e+00
## Q105655.fctrNo             0.000000e+00
## Q105655.fctrYes            0.000000e+00
## Q105840.fctrNo             0.000000e+00
## Q105840.fctrYes            0.000000e+00
## Q106042.fctrYes            0.000000e+00
## Q106272.fctrNo             0.000000e+00
## Q106272.fctrYes            0.000000e+00
## Q106388.fctrNo             0.000000e+00
## Q106388.fctrYes            0.000000e+00
## Q106389.fctrYes            0.000000e+00
## Q106993.fctrNo             0.000000e+00
## Q106993.fctrYes            0.000000e+00
## Q106997.fctrYy             0.000000e+00
## Q107491.fctrNo             0.000000e+00
## Q107491.fctrYes            0.000000e+00
## Q107869.fctrNo             0.000000e+00
## Q107869.fctrYes            0.000000e+00
## Q108342.fctrIn-person      0.000000e+00
## Q108342.fctrOnline         0.000000e+00
## Q108343.fctrNo             0.000000e+00
## Q108343.fctrYes            0.000000e+00
## Q108617.fctrNo             0.000000e+00
## Q108617.fctrYes            0.000000e+00
## Q108754.fctrNo             0.000000e+00
## Q108754.fctrYes            0.000000e+00
## Q108855.fctrUmm...         0.000000e+00
## Q108856.fctrSocialize      0.000000e+00
## Q108856.fctrSpace          0.000000e+00
## Q108950.fctrCautious       0.000000e+00
## Q108950.fctrRisk-friendly  0.000000e+00
## Q109367.fctrNo             0.000000e+00
## Q109367.fctrYes            0.000000e+00
## Q111220.fctrNo             0.000000e+00
## Q111580.fctrDemanding      0.000000e+00
## Q111580.fctrSupportive     0.000000e+00
## Q111848.fctrNo             0.000000e+00
## Q111848.fctrYes            0.000000e+00
## Q112270.fctrNo             0.000000e+00
## Q112270.fctrYes            0.000000e+00
## Q112478.fctrYes            0.000000e+00
## Q112512.fctrNo             0.000000e+00
## Q112512.fctrYes            0.000000e+00
## Q113583.fctrTalk           0.000000e+00
## Q113584.fctrPeople         0.000000e+00
## Q113584.fctrTechnology     0.000000e+00
## Q113992.fctrNo             0.000000e+00
## Q113992.fctrYes            0.000000e+00
## Q114152.fctrNo             0.000000e+00
## Q114152.fctrYes            0.000000e+00
## Q114386.fctrMysterious     0.000000e+00
## Q114386.fctrTMI            0.000000e+00
## Q114517.fctrNo             0.000000e+00
## Q114517.fctrYes            0.000000e+00
## Q114748.fctrNo             0.000000e+00
## Q114748.fctrYes            0.000000e+00
## Q114961.fctrNo             0.000000e+00
## Q114961.fctrYes            0.000000e+00
## Q115195.fctrNo             0.000000e+00
## Q115602.fctrNo             0.000000e+00
## Q115602.fctrYes            0.000000e+00
## Q115610.fctrNo             0.000000e+00
## Q115610.fctrYes            0.000000e+00
## Q115777.fctrEnd            0.000000e+00
## Q115777.fctrStart          0.000000e+00
## Q115899.fctrMe             0.000000e+00
## Q116197.fctrA.M.           0.000000e+00
## Q116197.fctrP.M.           0.000000e+00
## Q116441.fctrNo             0.000000e+00
## Q116441.fctrYes            0.000000e+00
## Q116448.fctrNo             0.000000e+00
## Q116448.fctrYes            0.000000e+00
## Q116601.fctrNo             0.000000e+00
## Q116601.fctrYes            0.000000e+00
## Q116797.fctrNo             0.000000e+00
## Q116797.fctrYes            0.000000e+00
## Q116953.fctrYes            0.000000e+00
## Q117186.fctrCool headed    0.000000e+00
## Q117186.fctrHot headed     0.000000e+00
## Q117193.fctrOdd hours      0.000000e+00
## Q117193.fctrStandard hours 0.000000e+00
## Q118117.fctrNo             0.000000e+00
## Q118117.fctrYes            0.000000e+00
## Q118232.fctrPr             0.000000e+00
## Q118233.fctrYes            0.000000e+00
## Q118237.fctrNo             0.000000e+00
## Q118237.fctrYes            0.000000e+00
## Q118892.fctrNo             0.000000e+00
## Q118892.fctrYes            0.000000e+00
## Q119334.fctrNo             0.000000e+00
## Q119334.fctrYes            0.000000e+00
## Q119650.fctrGiving         0.000000e+00
## Q119650.fctrReceiving      0.000000e+00
## Q119851.fctrYes            0.000000e+00
## Q120012.fctrNo             0.000000e+00
## Q120012.fctrYes            0.000000e+00
## Q120014.fctrNo             0.000000e+00
## Q120014.fctrYes            0.000000e+00
## Q120194.fctrTry first      0.000000e+00
## Q120472.fctrArt            0.000000e+00
## Q120650.fctrNo             0.000000e+00
## Q120978.fctrNo             0.000000e+00
## Q120978.fctrYes            0.000000e+00
## Q121011.fctrNo             0.000000e+00
## Q121011.fctrYes            0.000000e+00
## Q121699.fctrNo             0.000000e+00
## Q121700.fctrNo             0.000000e+00
## Q121700.fctrYes            0.000000e+00
## Q122120.fctrNo             0.000000e+00
## Q122769.fctrNo             0.000000e+00
## Q122769.fctrYes            0.000000e+00
## Q122770.fctrNo             0.000000e+00
## Q122770.fctrYes            0.000000e+00
## Q122771.fctrPc             0.000000e+00
## Q123464.fctrNo             0.000000e+00
## Q123464.fctrYes            0.000000e+00
## Q123621.fctrNo             0.000000e+00
## Q123621.fctrYes            0.000000e+00
## Q124122.fctrNo             0.000000e+00
## Q124122.fctrYes            0.000000e+00
## Q124742.fctrYes            0.000000e+00
## Q96024.fctrNo              0.000000e+00
## Q96024.fctrYes             0.000000e+00
## Q98059.fctrOnly-child      0.000000e+00
## Q98059.fctrYes             0.000000e+00
## Q98078.fctrNo              0.000000e+00
## Q98078.fctrYes             0.000000e+00
## Q98578.fctrNo              0.000000e+00
## Q98578.fctrYes             0.000000e+00
## Q98869.fctrYes             0.000000e+00
## Q99581.fctrNo              0.000000e+00
## Q99581.fctrYes             0.000000e+00
## Q99716.fctrNo              0.000000e+00
## Q99716.fctrYes             0.000000e+00
## Q99982.fctrCheck!          0.000000e+00
## Q99982.fctrNope            0.000000e+00
## YOB.Age.fctr.C             0.000000e+00
## YOB.Age.fctr.Q             0.000000e+00
## YOB.Age.fctr^4             0.000000e+00
## YOB.Age.fctr^5             0.000000e+00
## YOB.Age.fctr^6             0.000000e+00
if (glb_is_classification && glb_is_binomial)
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId, 
            prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId, 
                                         "opt.prob.threshold.OOB"]) else
    glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)                  
## Warning in glb_analytics_diag_plots(obs_df = glbObsTrn, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 107

## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
##   USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1    1309          D                         0.2170436
## 2    2641          D                         0.2162383
## 3    1311          D                         0.2067811
## 4    1393          D                                NA
## 5    3006          D                         0.2212405
## 6    4956          D                         0.2302791
##   Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1                            R                             TRUE
## 2                            R                             TRUE
## 3                            R                             TRUE
## 4                         <NA>                               NA
## 5                            R                             TRUE
## 6                            R                             TRUE
##   Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1                            0.7829564                               FALSE
## 2                            0.7837617                               FALSE
## 3                            0.7932189                               FALSE
## 4                                   NA                                  NA
## 5                            0.7787595                               FALSE
## 6                            0.7697209                               FALSE
##   Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1                         0.2061977                            R
## 2                         0.2256276                            R
## 3                         0.2257340                            R
## 4                         0.2264941                            R
## 5                         0.2283662                            R
## 6                         0.2306508                            R
##   Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1                             TRUE                            0.7938023
## 2                             TRUE                            0.7743724
## 3                             TRUE                            0.7742660
## 4                             TRUE                            0.7735059
## 5                             TRUE                            0.7716338
## 6                             TRUE                            0.7693492
##   Party.fctr.Final..rcv.glmnet.is.acc
## 1                               FALSE
## 2                               FALSE
## 3                               FALSE
## 4                               FALSE
## 5                               FALSE
## 6                               FALSE
##   Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1                                 FALSE                         -0.4438023
## 2                                 FALSE                         -0.4243724
## 3                                 FALSE                         -0.4242660
## 4                                 FALSE                         -0.4235059
## 5                                 FALSE                         -0.4216338
## 6                                 FALSE                         -0.4193492
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 776       25          D                         0.4496762
## 1017     695          D                         0.5175311
## 1386    3812          D                         0.5406492
## 1650    1729          D                         0.6298912
## 1912    4924          D                         0.5540143
## 2203    2226          R                         0.7798730
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 776                             R                             TRUE
## 1017                            R                             TRUE
## 1386                            R                             TRUE
## 1650                            R                             TRUE
## 1912                            R                             TRUE
## 2203                            D                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 776                             0.5503238
## 1017                            0.4824689
## 1386                            0.4593508
## 1650                            0.3701088
## 1912                            0.4459857
## 2203                            0.7798730
##      Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 776                                FALSE                         0.4920116
## 1017                               FALSE                         0.5154723
## 1386                               FALSE                         0.5440174
## 1650                               FALSE                         0.5686217
## 1912                               FALSE                         0.6015241
## 2203                               FALSE                         0.7291472
##      Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 776                             R                             TRUE
## 1017                            R                             TRUE
## 1386                            R                             TRUE
## 1650                            R                             TRUE
## 1912                            R                             TRUE
## 2203                            D                             TRUE
##      Party.fctr.Final..rcv.glmnet.err.abs
## 776                             0.5079884
## 1017                            0.4845277
## 1386                            0.4559826
## 1650                            0.4313783
## 1912                            0.3984759
## 2203                            0.7291472
##      Party.fctr.Final..rcv.glmnet.is.acc
## 776                                FALSE
## 1017                               FALSE
## 1386                               FALSE
## 1650                               FALSE
## 1912                               FALSE
## 2203                               FALSE
##      Party.fctr.Final..rcv.glmnet.accurate
## 776                                  FALSE
## 1017                                 FALSE
## 1386                                 FALSE
## 1650                                 FALSE
## 1912                                 FALSE
## 2203                                 FALSE
##      Party.fctr.Final..rcv.glmnet.error
## 776                         -0.15798844
## 1017                        -0.13452765
## 1386                        -0.10598258
## 1650                        -0.08137834
## 1912                        -0.04847587
## 2203                         0.07914719
##      USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2330    1307          R                                NA
## 2331    2749          R                         0.8687514
## 2332    1236          R                         0.8691283
## 2333    1515          R                         0.8899312
## 2334    3895          R                         0.8914540
## 2335     626          R                         0.9082257
##      Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2330                         <NA>                               NA
## 2331                            D                             TRUE
## 2332                            D                             TRUE
## 2333                            D                             TRUE
## 2334                            D                             TRUE
## 2335                            D                             TRUE
##      Party.fctr.All.X..rcv.glmnet.err.abs
## 2330                                   NA
## 2331                            0.8687514
## 2332                            0.8691283
## 2333                            0.8899312
## 2334                            0.8914540
## 2335                            0.9082257
##      Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2330                                  NA                         0.8776340
## 2331                               FALSE                         0.8803600
## 2332                               FALSE                         0.8807433
## 2333                               FALSE                         0.8830267
## 2334                               FALSE                         0.8964361
## 2335                               FALSE                         0.8990037
##      Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2330                            D                             TRUE
## 2331                            D                             TRUE
## 2332                            D                             TRUE
## 2333                            D                             TRUE
## 2334                            D                             TRUE
## 2335                            D                             TRUE
##      Party.fctr.Final..rcv.glmnet.err.abs
## 2330                            0.8776340
## 2331                            0.8803600
## 2332                            0.8807433
## 2333                            0.8830267
## 2334                            0.8964361
## 2335                            0.8990037
##      Party.fctr.Final..rcv.glmnet.is.acc
## 2330                               FALSE
## 2331                               FALSE
## 2332                               FALSE
## 2333                               FALSE
## 2334                               FALSE
## 2335                               FALSE
##      Party.fctr.Final..rcv.glmnet.accurate
## 2330                                 FALSE
## 2331                                 FALSE
## 2332                                 FALSE
## 2333                                 FALSE
## 2334                                 FALSE
## 2335                                 FALSE
##      Party.fctr.Final..rcv.glmnet.error
## 2330                          0.2276340
## 2331                          0.2303600
## 2332                          0.2307433
## 2333                          0.2330267
## 2334                          0.2464361
## 2335                          0.2490037

dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
    dsp_feats_vctr <- union(dsp_feats_vctr, 
                            glb_feats_df[!is.na(glb_feats_df[, var]), "id"])

# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids, 
#                     grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])

print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "Party.fctr.Final..rcv.glmnet.prob"   
## [2] "Party.fctr.Final..rcv.glmnet"        
## [3] "Party.fctr.Final..rcv.glmnet.err"    
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]

print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
    # Merge or cbind ?
    glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
    
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]); 

replay.petrisim(pn = glb_analytics_pn, 
    replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, 
        "data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans    "bgn " "fit.data.training.all " "predict.data.new " "end " 
## 0.0000   multiple enabled transitions:  data.training.all data.new model.selected    firing:  model.selected 
## 1.0000    3   2 1 0 0 
## 1.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  data.training.all.prediction 
## 2.0000    5   2 0 0 1
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token
## 2.0000   multiple enabled transitions:  data.training.all data.new model.selected model.final data.training.all.prediction   firing:  model.final 
## 3.0000    4   2 0 1 1

glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
##                label step_major step_minor label_minor     bgn     end
## 9  fit.data.training          5          1           1 318.738 328.479
## 10  predict.data.new          6          0           0 328.479      NA
##    elapsed
## 9    9.741
## 10      NA

Step 6.0: predict data new

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.65

## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.65
## Warning in glb_analytics_diag_plots(obs_df = glbObsNew, mdl_id =
## glbMdlFinId, : Limiting important feature scatter plots to 5 out of 107
## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## Warning: Removed 1392 rows containing missing values (geom_point).

## NULL
## Loading required package: tidyr
## 
## Attaching package: 'tidyr'
## The following object is masked from 'package:Matrix':
## 
##     expand
## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"
## [1] 0.65
## [1] "glbMdlSelId: All.X##rcv#glmnet"
## [1] "glbMdlFinId: Final##rcv#glmnet"
## [1] "Cross Validation issues:"
##        MFO###myMFO_classfr  Random###myrandom_classfr 
##                          0                          0 
## Max.cor.Y.rcv.1X1###glmnet 
##                          0
##                                 max.Accuracy.OOB max.AUCROCR.OOB
## Low.cor.X##rcv#glmnet                  0.5812500       0.3157430
## All.X##rcv#glmnet                      0.5812500       0.3157430
## Interact.High.cor.Y##rcv#glmnet        0.5732143       0.3571392
## All.X##rcv#glm                         0.5696429       0.3371452
## Max.cor.Y##rcv#rpart                   0.5633929       0.3774772
## Max.cor.Y.rcv.1X1###glmnet             0.5633929       0.3658672
## Random###myrandom_classfr              0.4696429       0.5191202
## MFO###myMFO_classfr                    0.4696429       0.5000000
## Final##rcv#glmnet                             NA              NA
##                                 max.AUCpROC.OOB max.Accuracy.fit
## Low.cor.X##rcv#glmnet                 0.6261026        0.6254518
## All.X##rcv#glmnet                     0.6261026        0.6254518
## Interact.High.cor.Y##rcv#glmnet       0.6031353        0.6058167
## All.X##rcv#glm                        0.5989777        0.6049923
## Max.cor.Y##rcv#rpart                  0.5896897        0.6000450
## Max.cor.Y.rcv.1X1###glmnet            0.5896897        0.5721673
## Random###myrandom_classfr             0.5235690        0.4700989
## MFO###myMFO_classfr                   0.5000000        0.4700989
## Final##rcv#glmnet                            NA        0.6343978
##                                 opt.prob.threshold.fit
## Low.cor.X##rcv#glmnet                             0.60
## All.X##rcv#glmnet                                 0.60
## Interact.High.cor.Y##rcv#glmnet                   0.65
## All.X##rcv#glm                                    0.65
## Max.cor.Y##rcv#rpart                              0.55
## Max.cor.Y.rcv.1X1###glmnet                        0.60
## Random###myrandom_classfr                         0.55
## MFO###myMFO_classfr                               0.50
## Final##rcv#glmnet                                 0.60
##                                 opt.prob.threshold.OOB
## Low.cor.X##rcv#glmnet                             0.65
## All.X##rcv#glmnet                                 0.65
## Interact.High.cor.Y##rcv#glmnet                   0.65
## All.X##rcv#glm                                    0.75
## Max.cor.Y##rcv#rpart                              0.55
## Max.cor.Y.rcv.1X1###glmnet                        0.60
## Random###myrandom_classfr                         0.55
## MFO###myMFO_classfr                               0.50
## Final##rcv#glmnet                                   NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
##          Prediction
## Reference   R   D
##         R 476  50
##         D 419 175
##     err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## PKy        24.55182         4.44135        29.00365              NA
## PKn        54.28673        14.22010        70.27487              NA
## N         169.17762        37.99992       208.01690              NA
## SKn       855.61896       233.08138      1093.06362              NA
## MKn       227.85830        61.65902       289.98913              NA
## SKy        61.84682        23.64664        86.84782              NA
## MKy       572.19199       130.77361       706.07068              NA
##     .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## PKy     0.01169065    0.008035714    0.007183908     52        3        7
## PKn     0.03372302    0.026785714    0.026580460    150       16       21
## N       0.08250899    0.074107143    0.073275862    367       11       91
## SKn     0.43165468    0.456250000    0.458333333   1920      120      518
## MKn     0.11600719    0.121428571    0.121408046    516       28      141
## SKy     0.03304856    0.047321429    0.046695402    147       11       54
## MKy     0.29136691    0.266071429    0.266522989   1296       49      322
##     .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## PKy      9       35       26     10     52     10     61        0.4934833
## PKn     30      131       49     37    150     37    180        0.4740034
## N       83      230      220    102    367    102    450        0.4578304
## SKn    511     1340     1091    638   1920    638   2431        0.4561280
## MKn    136      344      308    169    516    169    652        0.4533752
## SKy     53      119       81     65    147     65    200        0.4461630
## MKy    298      752      842    371   1296    371   1594        0.4388376
##     err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## PKy        0.4721503               NA        0.4754696
## PKn        0.3619116               NA        0.3904159
## N          0.4609745               NA        0.4622598
## SKn        0.4456349               NA        0.4496354
## MKn        0.4415859               NA        0.4447686
## SKy        0.4207267               NA        0.4342391
## MKy        0.4415062               NA        0.4429553
##  err.abs.fit.sum  err.abs.OOB.sum  err.abs.trn.sum  err.abs.new.sum 
##      1965.532248       505.822031      2483.266662               NA 
##   .freqRatio.Fit   .freqRatio.OOB   .freqRatio.Tst           .n.Fit 
##         1.000000         1.000000         1.000000      4448.000000 
##         .n.New.D         .n.New.R           .n.OOB         .n.Trn.D 
##       238.000000      1154.000000      1120.000000      2951.000000 
##         .n.Trn.R           .n.Tst           .n.fit           .n.new 
##      2617.000000      1392.000000      4448.000000      1392.000000 
##           .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean 
##      5568.000000         3.219821         3.044490               NA 
## err.abs.trn.mean 
##         3.099744
## [1] "Features Importance for selected models:"
##                   All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Q109244.fctrYes               100.00000           100.0000000
## Hhold.fctrPKn                  67.44709            41.1135979
## Q109244.fctrNo                 48.99892            38.5639836
## Q115611.fctrYes                42.94308            35.9448112
## Q98869.fctrNo                  34.81660            20.5482906
## Q98197.fctrNo                  24.12262            30.9612021
## Q116881.fctrRight              18.54237            18.0112480
## Hhold.fctrMKy                  18.29301             9.1538254
## Q99480.fctrNo                  17.61676             4.0427224
## Income.fctr.C                  16.96770             7.8080557
## YOB.Age.fctr.L                 16.78320             7.0997165
## Q115611.fctrNo                 15.93462            14.6199445
## Q118232.fctrId                 15.69161            10.8720293
## Q120379.fctrYes                15.41041             9.7198751
## Q122771.fctrPt                 15.02455             8.5254872
## Q119851.fctrNo                 14.85551            11.0687511
## Edn.fctr^4                     14.60100             0.0000000
## Q101163.fctrDad                14.59198            10.2622333
## Hhold.fctrSKy                  14.46184             0.0000000
## Q113181.fctrYes                13.34329            15.5145602
## Q111220.fctrYes                13.25530             0.1522855
## Q100689.fctrYes                12.51301             8.7005370
## Q110740.fctrPC                 12.43709             7.7413099
## Q113181.fctrNo                 12.37742            11.4636192
## Q106997.fctrYy                 11.33992             0.0000000
## Income.fctr.Q                  11.26401             6.7052356
## Q115390.fctrNo                 11.05396             1.0445476
## Q98197.fctrYes                 10.98513             0.2579574
## Q115899.fctrCs                 10.71373             5.4716495
## Gender.fctrM                   10.43808            11.8191970
## Q116881.fctrHappy              10.21148             2.0532717
## Q101163.fctrMom                10.10753             6.0972450
## [1] "glbObsNew prediction stats:"
## 
##    R    D 
## 1154  238
##                   label step_major step_minor label_minor     bgn     end
## 10     predict.data.new          6          0           0 328.479 343.606
## 11 display.session.info          7          0           0 343.606      NA
##    elapsed
## 10  15.127
## 11      NA

Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.

##                      label step_major step_minor label_minor     bgn
## 2  partition.data.training          2          0           0   9.292
## 4               fit.models          4          0           0 124.554
## 5               fit.models          4          1           1 198.236
## 8        fit.data.training          5          0           0 281.996
## 10        predict.data.new          6          0           0 328.479
## 6               fit.models          4          2           2 264.676
## 9        fit.data.training          5          1           1 318.738
## 3          select.features          3          0           0 118.368
## 7               fit.models          4          3           3 277.359
## 1             cluster.data          1          0           0   8.050
##        end elapsed duration
## 2  118.368 109.076  109.076
## 4  198.235  73.681   73.681
## 5  264.675  66.439   66.439
## 8  318.737  36.741   36.741
## 10 343.606  15.127   15.127
## 6  277.359  12.683   12.683
## 9  328.479   9.741    9.741
## 3  124.554   6.186    6.186
## 7  281.996   4.637    4.637
## 1    9.292   1.242    1.242
## [1] "Total Elapsed Time: 343.606 secs"